lattice connection The coding connection Recent asym ① 1 = 499, ✿ ✿ ✿ , ① 12 = 9413. A weight- ✇ subset-sum problem: Eurocrypt ▲ ✒ Z 12 as Is there a subsequence of Howgrave-Graham–Joux: (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ subset-sum ✙ ✿ ❢ ✈ ✈ ① 1 + ✁ ✁ ✁ + ✈ 12 ① 12 = 0 ❣ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) (Incorrect ✙ ✿ ✉ ✷ Z 12 as having length ✇ and sum 36634? Eurocrypt ❀ ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0). Replace Z with ( Z ❂ 2) ♠ : Becker–Co ❏ ✒ ❢ 1 ❀ 2 ❀ ✿ ✿ ✿ ❀ 12 ❣ Is there a subsequence of subset-sum ✙ ✿ P ✐ ✷ ❏ ① ✐ = 36634 then (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ ✈ ✷ ▲ where ✈ ✐ = ✉ ✐ � [ ✐ ✷ ❏ ]. Adaptations 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) Asiacrypt ✈ very close to ✉ . having length ✇ and xor 1060? Thomae, Reasonable to hope that This is the central algorithmic Becker–Joux–Ma the closest vector in ▲ to ✉ . ✈ problem in coding theory. Subset-sum algorithms ✙ dimension-1 CVP algorithms.
connection The coding connection Recent asymptotic ① 499, ✿ ✿ ✿ , ① 12 = 9413. A weight- ✇ subset-sum problem: Eurocrypt 2010 Is there a subsequence of Howgrave-Graham–Joux: ▲ ✒ as (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ subset-sum exponent ✙ ✿ ❢ ✈ ✈ ① ✁ ✁ ✁ ✈ 12 ① 12 = 0 ❣ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) (Incorrect claim: ✙ ✿ ✉ ✷ as having length ✇ and sum 36634? Eurocrypt 2011 ❀ ❀ ❀ ❀ ❀ ❀ ❀ 0 ❀ 0 ❀ 0 ❀ 0 ❀ 0). Replace Z with ( Z ❂ 2) ♠ : Becker–Coron–Jou ❏ ✒ ❢ ❀ ❀ ✿ ✿ ✿ ❀ 12 ❣ Is there a subsequence of subset-sum exponent ✙ ✿ P ✐ ✷ ❏ ① ✐ 36634 then (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ ✈ ✷ ▲ ✈ ✐ = ✉ ✐ � [ ✐ ✷ ❏ ]. Adaptations to deco 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) Asiacrypt 2011 Ma ✈ to ✉ . having length ✇ and xor 1060? Thomae, Eurocrypt hope that This is the central algorithmic Becker–Joux–May–Meurer. vector in ▲ to ✉ . ✈ problem in coding theory. rithms ✙ CVP algorithms.
The coding connection Recent asymptotic news ① ✿ ✿ ✿ ① = 9413. A weight- ✇ subset-sum problem: Eurocrypt 2010 Is there a subsequence of Howgrave-Graham–Joux: ▲ ✒ (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ subset-sum exponent ✙ 0 ✿ 337. ❢ ✈ ✈ ① ✁ ✁ ✁ ✈ ① 0 ❣ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) (Incorrect claim: ✙ 0 ✿ 311.) ✉ ✷ having length ✇ and sum 36634? Eurocrypt 2011 ❀ ❀ ❀ ❀ ❀ ❀ ❀ ❀ ❀ ❀ ❀ 0). Replace Z with ( Z ❂ 2) ♠ : Becker–Coron–Joux: ❏ ✒ ❢ ❀ ❀ ✿ ✿ ✿ ❀ ❣ Is there a subsequence of subset-sum exponent ✙ 0 ✿ 291. P ✐ ✷ ❏ ① ✐ then (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ ✈ ✷ ▲ ✈ ✐ ✉ ✐ � ✐ ✷ ❏ ]. Adaptations to decoding: 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) Asiacrypt 2011 May–Meurer– ✈ ✉ having length ✇ and xor 1060? Thomae, Eurocrypt 2012 This is the central algorithmic Becker–Joux–May–Meurer. ▲ to ✉ . ✈ problem in coding theory. ✙ rithms.
The coding connection Recent asymptotic news A weight- ✇ subset-sum problem: Eurocrypt 2010 Is there a subsequence of Howgrave-Graham–Joux: (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ subset-sum exponent ✙ 0 ✿ 337. 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) (Incorrect claim: ✙ 0 ✿ 311.) having length ✇ and sum 36634? Eurocrypt 2011 Replace Z with ( Z ❂ 2) ♠ : Becker–Coron–Joux: Is there a subsequence of subset-sum exponent ✙ 0 ✿ 291. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Adaptations to decoding: 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) Asiacrypt 2011 May–Meurer– having length ✇ and xor 1060? Thomae, Eurocrypt 2012 This is the central algorithmic Becker–Joux–May–Meurer. problem in coding theory.
ding connection Recent asymptotic news Post-quantum eight- ✇ subset-sum problem: Eurocrypt 2010 Claimed there a subsequence of Howgrave-Graham–Joux: Lyubashevsky–P ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ subset-sum exponent ✙ 0 ✿ 337. “Public-k ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) (Incorrect claim: ✙ 0 ✿ 311.) primitives length ✇ and sum 36634? as secure Eurocrypt 2011 Replace Z with ( Z ❂ 2) ♠ : Becker–Coron–Joux: There ar there a subsequence of subset-sum exponent ✙ 0 ✿ 291. quantum ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ better than Adaptations to decoding: ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) on the subset Asiacrypt 2011 May–Meurer– length ✇ and xor 1060? Thomae, Eurocrypt 2012 Hmmm. the central algorithmic Becker–Joux–May–Meurer. quantum roblem in coding theory.
connection Recent asymptotic news Post-quantum subset ✇ subset-sum problem: Eurocrypt 2010 Claimed in TCC 2010 subsequence of Howgrave-Graham–Joux: Lyubashevsky–Palacio–Segev ❀ ❀ ❀ 2535 ❀ 3596 ❀ 3608 ❀ subset-sum exponent ✙ 0 ✿ 337. “Public-key cryptograph ❀ ❀ ❀ 7353 ❀ 7650 ❀ 9413) (Incorrect claim: ✙ 0 ✿ 311.) primitives provably ✇ and sum 36634? as secure as subset Eurocrypt 2011 ( Z ❂ 2) ♠ : Becker–Coron–Joux: There are “currently subsequence of subset-sum exponent ✙ 0 ✿ 291. quantum algorithms ❀ ❀ ❀ 2535 ❀ 3596 ❀ 3608 ❀ better than classical Adaptations to decoding: ❀ ❀ ❀ 7353 ❀ 7650 ❀ 9413) on the subset sum Asiacrypt 2011 May–Meurer– ✇ and xor 1060? Thomae, Eurocrypt 2012 Hmmm. What’s the central algorithmic Becker–Joux–May–Meurer. quantum subset-sum ding theory.
Recent asymptotic news Post-quantum subset sum ✇ roblem: Eurocrypt 2010 Claimed in TCC 2010 Howgrave-Graham–Joux: Lyubashevsky–Palacio–Segev ❀ ❀ ❀ ❀ ❀ 3608 ❀ subset-sum exponent ✙ 0 ✿ 337. “Public-key cryptographic ❀ ❀ ❀ ❀ 7650 ❀ 9413) (Incorrect claim: ✙ 0 ✿ 311.) primitives provably ✇ 36634? as secure as subset sum”: Eurocrypt 2011 ♠ ❂ Becker–Coron–Joux: There are “currently no known subset-sum exponent ✙ 0 ✿ 291. quantum algorithms that perfo ❀ ❀ ❀ ❀ ❀ 3608 ❀ better than classical ones Adaptations to decoding: ❀ ❀ ❀ ❀ 7650 ❀ 9413) on the subset sum problem”. Asiacrypt 2011 May–Meurer– ✇ 1060? Thomae, Eurocrypt 2012 Hmmm. What’s the best rithmic Becker–Joux–May–Meurer. quantum subset-sum exponent?
Recent asymptotic news Post-quantum subset sum Eurocrypt 2010 Claimed in TCC 2010 Howgrave-Graham–Joux: Lyubashevsky–Palacio–Segev subset-sum exponent ✙ 0 ✿ 337. “Public-key cryptographic (Incorrect claim: ✙ 0 ✿ 311.) primitives provably as secure as subset sum”: Eurocrypt 2011 Becker–Coron–Joux: There are “currently no known subset-sum exponent ✙ 0 ✿ 291. quantum algorithms that perform better than classical ones Adaptations to decoding: on the subset sum problem”. Asiacrypt 2011 May–Meurer– Thomae, Eurocrypt 2012 Hmmm. What’s the best Becker–Joux–May–Meurer. quantum subset-sum exponent?
Recent asymptotic news Post-quantum subset sum Quantum crypt 2010 Claimed in TCC 2010 Assume ❢ wgrave-Graham–Joux: Lyubashevsky–Palacio–Segev has ♥ -bit subset-sum exponent ✙ 0 ✿ 337. “Public-key cryptographic Generic b rrect claim: ✙ 0 ✿ 311.) primitives provably finds this as secure as subset sum”: ✙ 2 ♥ evaluations crypt 2011 ❢ er–Coron–Joux: There are “currently no known 1996 Grover subset-sum exponent ✙ 0 ✿ 291. quantum algorithms that perform finds this better than classical ones ✙ 2 0 ✿ 5 ♥ quantum Adaptations to decoding: ❢ on the subset sum problem”. Asiacrypt 2011 May–Meurer– on superp Thomae, Eurocrypt 2012 Hmmm. What’s the best Cost of quantu ❢ er–Joux–May–Meurer. quantum subset-sum exponent? ✙ cost of ❢ if cost counts
ptotic news Post-quantum subset sum Quantum search (0.5) Claimed in TCC 2010 Assume that function ❢ wgrave-Graham–Joux: Lyubashevsky–Palacio–Segev has ♥ -bit input, unique onent ✙ 0 ✿ 337. “Public-key cryptographic Generic brute-force ✙ 0 ✿ 311.) primitives provably finds this root using as secure as subset sum”: ✙ 2 ♥ evaluations of ❢ oux: There are “currently no known 1996 Grover metho onent ✙ 0 ✿ 291. quantum algorithms that perform finds this root using better than classical ones ✙ 2 0 ✿ 5 ♥ quantum evaluations ecoding: ❢ on the subset sum problem”. May–Meurer– on superpositions of crypt 2012 Hmmm. What’s the best Cost of quantum evaluation ❢ er–Joux–May–Meurer. quantum subset-sum exponent? ✙ cost of evaluation ❢ if cost counts qubit
Post-quantum subset sum Quantum search (0.5) Claimed in TCC 2010 Assume that function ❢ Lyubashevsky–Palacio–Segev has ♥ -bit input, unique root. ✙ ✿ 337. “Public-key cryptographic Generic brute-force search ✙ ✿ primitives provably finds this root using as secure as subset sum”: ✙ 2 ♥ evaluations of ❢ . There are “currently no known 1996 Grover method ✙ ✿ 291. quantum algorithms that perform finds this root using better than classical ones ✙ 2 0 ✿ 5 ♥ quantum evaluations ❢ on the subset sum problem”. y–Meurer– on superpositions of inputs. Hmmm. What’s the best Cost of quantum evaluation ❢ y–Meurer. quantum subset-sum exponent? ✙ cost of evaluation of ❢ if cost counts qubit “operations”.
Post-quantum subset sum Quantum search (0.5) Claimed in TCC 2010 Assume that function ❢ Lyubashevsky–Palacio–Segev has ♥ -bit input, unique root. “Public-key cryptographic Generic brute-force search primitives provably finds this root using as secure as subset sum”: ✙ 2 ♥ evaluations of ❢ . There are “currently no known 1996 Grover method quantum algorithms that perform finds this root using better than classical ones ✙ 2 0 ✿ 5 ♥ quantum evaluations of ❢ on the subset sum problem”. on superpositions of inputs. Hmmm. What’s the best Cost of quantum evaluation of ❢ quantum subset-sum exponent? ✙ cost of evaluation of ❢ if cost counts qubit “operations”.
ost-quantum subset sum Quantum search (0.5) Easily adapt different Claimed in TCC 2010 Assume that function ❢ and # not Lyubashevsky–Palacio–Segev has ♥ -bit input, unique root. Faster if “Public-key cryptographic Generic brute-force search but typic rimitives provably finds this root using Most interesting: ✷ ❢ ❀ ❣ secure as subset sum”: ✙ 2 ♥ evaluations of ❢ . are “currently no known 1996 Grover method quantum algorithms that perform finds this root using than classical ones ✙ 2 0 ✿ 5 ♥ quantum evaluations of ❢ subset sum problem”. on superpositions of inputs. Hmmm. What’s the best Cost of quantum evaluation of ❢ quantum subset-sum exponent? ✙ cost of evaluation of ❢ if cost counts qubit “operations”.
subset sum Quantum search (0.5) Easily adapt to handle different # of roots, 2010 Assume that function ❢ and # not known alacio–Segev has ♥ -bit input, unique root. Faster if # is large, cryptographic Generic brute-force search but typically # is not rovably finds this root using Most interesting: # ✷ ❢ ❀ ❣ subset sum”: ✙ 2 ♥ evaluations of ❢ . “currently no known 1996 Grover method rithms that perform finds this root using classical ones ✙ 2 0 ✿ 5 ♥ quantum evaluations of ❢ sum problem”. on superpositions of inputs. the best Cost of quantum evaluation of ❢ subset-sum exponent? ✙ cost of evaluation of ❢ if cost counts qubit “operations”.
Quantum search (0.5) Easily adapt to handle different # of roots, Assume that function ❢ and # not known in advance. alacio–Segev has ♥ -bit input, unique root. Faster if # is large, Generic brute-force search but typically # is not very la finds this root using Most interesting: # ✷ ❢ 0 ❀ 1 ❣ ✙ 2 ♥ evaluations of ❢ . known 1996 Grover method perform finds this root using ✙ 2 0 ✿ 5 ♥ quantum evaluations of ❢ m”. on superpositions of inputs. Cost of quantum evaluation of ❢ onent? ✙ cost of evaluation of ❢ if cost counts qubit “operations”.
Quantum search (0.5) Easily adapt to handle different # of roots, Assume that function ❢ and # not known in advance. has ♥ -bit input, unique root. Faster if # is large, Generic brute-force search but typically # is not very large. finds this root using Most interesting: # ✷ ❢ 0 ❀ 1 ❣ . ✙ 2 ♥ evaluations of ❢ . 1996 Grover method finds this root using ✙ 2 0 ✿ 5 ♥ quantum evaluations of ❢ on superpositions of inputs. Cost of quantum evaluation of ❢ ✙ cost of evaluation of ❢ if cost counts qubit “operations”.
Quantum search (0.5) Easily adapt to handle different # of roots, Assume that function ❢ and # not known in advance. has ♥ -bit input, unique root. Faster if # is large, Generic brute-force search but typically # is not very large. finds this root using Most interesting: # ✷ ❢ 0 ❀ 1 ❣ . ✙ 2 ♥ evaluations of ❢ . Apply to the function 1996 Grover method ❏ ✼✦ Σ( ❏ ) � t where finds this root using Σ( ❏ ) = P ✐ ✷ ❏ ① ✐ . ✙ 2 0 ✿ 5 ♥ quantum evaluations of ❢ Cost 2 0 ✿ 5 ♥ to find root (i.e., on superpositions of inputs. to find indices of subsequence Cost of quantum evaluation of ❢ of ① 1 ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) ✙ cost of evaluation of ❢ or to decide that no root exists. if cost counts qubit “operations”. We suppress poly factors in cost.
Quantum search (0.5) Easily adapt to handle Algorithm different # of roots, Represent ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥ ❣ Assume that function ❢ and # not known in advance. ♥ � integer b ♥ -bit input, unique root. Faster if # is large, ♥ bits are Generic brute-force search but typically # is not very large. to store this root using Most interesting: # ✷ ❢ 0 ❀ 1 ❣ . ✙ ♥ evaluations of ❢ . ♥ qubits Apply to the function a superp ❏ Grover method ❏ ✼✦ Σ( ❏ ) � t where 2 ♥ complex this root using Σ( ❏ ) = P ✐ ✷ ❏ ① ✐ . ✿ ♥ quantum evaluations of ❢ ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ ♥ � ✙ Cost 2 0 ✿ 5 ♥ to find root (i.e., ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ ❥ ❛ ♥ � ❥ erpositions of inputs. to find indices of subsequence Measuring ♥ of quantum evaluation of ❢ of ① 1 ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) has chance ❥ ❛ ❏ ❥ ❏ ✙ cost of evaluation of ❢ or to decide that no root exists. Start from counts qubit “operations”. We suppress poly factors in cost. i.e., ❛ ❏ = ❂ ♥❂ ❏
(0.5) Easily adapt to handle Algorithm details fo different # of roots, Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ function ❢ and # not known in advance. ♥ � integer between 0 ♥ unique root. Faster if # is large, ♥ bits are enough rce search but typically # is not very large. to store one such integer. using Most interesting: # ✷ ❢ 0 ❀ 1 ❣ . ✙ ♥ of ❢ . ♥ qubits store much Apply to the function a superposition over ❏ method ❏ ✼✦ Σ( ❏ ) � t where 2 ♥ complex amplitudes using Σ( ❏ ) = P ✐ ✷ ❏ ① ✐ . ✿ ♥ ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with ✙ evaluations of ❢ Cost 2 0 ✿ 5 ♥ to find root (i.e., ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � ❥ ositions of inputs. to find indices of subsequence Measuring these ♥ evaluation of ❢ of ① 1 ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) has chance ❥ ❛ ❏ ❥ 2 to ❏ ✙ evaluation of ❢ or to decide that no root exists. Start from uniform qubit “operations”. We suppress poly factors in cost. i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 fo ❏
Easily adapt to handle Algorithm details for unique different # of roots, Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as ❢ and # not known in advance. integer between 0 and 2 ♥ � ♥ ot. Faster if # is large, ♥ bits are enough space but typically # is not very large. to store one such integer. Most interesting: # ✷ ❢ 0 ❀ 1 ❣ . ✙ ♥ ❢ ♥ qubits store much more, Apply to the function a superposition over sets ❏ : ❏ ✼✦ Σ( ❏ ) � t where 2 ♥ complex amplitudes Σ( ❏ ) = P ✐ ✷ ❏ ① ✐ . ✿ ♥ ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with ✙ evaluations of ❢ Cost 2 0 ✿ 5 ♥ to find root (i.e., ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. inputs. to find indices of subsequence Measuring these ♥ qubits evaluation of ❢ of ① 1 ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) has chance ❥ ❛ ❏ ❥ 2 to produce ❏ ✙ ❢ or to decide that no root exists. Start from uniform superposition, erations”. We suppress poly factors in cost. i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ .
Easily adapt to handle Algorithm details for unique root: different # of roots, Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an and # not known in advance. integer between 0 and 2 ♥ � 1. Faster if # is large, ♥ bits are enough space but typically # is not very large. to store one such integer. Most interesting: # ✷ ❢ 0 ❀ 1 ❣ . ♥ qubits store much more, Apply to the function a superposition over sets ❏ : ❏ ✼✦ Σ( ❏ ) � t where 2 ♥ complex amplitudes Σ( ❏ ) = P ✐ ✷ ❏ ① ✐ . ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with Cost 2 0 ✿ 5 ♥ to find root (i.e., ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. to find indices of subsequence Measuring these ♥ qubits of ① 1 ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) has chance ❥ ❛ ❏ ❥ 2 to produce ❏ . or to decide that no root exists. Start from uniform superposition, We suppress poly factors in cost. i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ .
adapt to handle Algorithm details for unique root: Step 1: ❛ ✥ ❜ different # of roots, ❜ ❏ = � ❛ ❏ ❏ t Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an not known in advance. ❜ ❏ = ❛ ❏ integer between 0 and 2 ♥ � 1. if # is large, This is ab ♥ bits are enough space ypically # is not very large. as computing to store one such integer. interesting: # ✷ ❢ 0 ❀ 1 ❣ . Step 2: ♥ qubits store much more, to the function Set ❛ ✥ ❜ a superposition over sets ❏ : ❂ ♥ P ❏ ✼✦ Σ( ❏ ) � t where ❜ ❏ = � ❛ ❏ ■ ❛ ■ 2 ♥ complex amplitudes P ✐ ✷ ❏ ① ✐ . This is also ❏ ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with 0 ✿ 5 ♥ to find root (i.e., Repeat steps ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. ✿ ♥ indices of subsequence about 0 ✿ ✁ Measuring these ♥ qubits ① ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) has chance ❥ ❛ ❏ ❥ 2 to produce ❏ . Measure ♥ decide that no root exists. With high Start from uniform superposition, suppress poly factors in cost. i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ . the unique ❏ ❏ t
handle Algorithm details for unique root: Step 1: Set ❛ ✥ ❜ ots, ❜ ❏ = � ❛ ❏ if Σ( ❏ ) t Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an wn in advance. ❜ ❏ = ❛ ❏ otherwise. integer between 0 and 2 ♥ � 1. rge, This is about as easy ♥ bits are enough space is not very large. as computing Σ. to store one such integer. interesting: # ✷ ❢ 0 ❀ 1 ❣ . Step 2: “Grover diffusion”. ♥ qubits store much more, function Set ❛ ✥ ❜ where a superposition over sets ❏ : ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ P ❏ ✼✦ ❏ � t where ■ ❛ ■ 2 ♥ complex amplitudes P ✐ ✷ ❏ ① ✐ . This is also easy. ❏ ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with ✿ ♥ find root (i.e., Repeat steps 1 and ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ of subsequence Measuring these ♥ qubits ① ❀ ✿ ✿ ✿ ❀ ① ♥ with sum t ) has chance ❥ ❛ ❏ ❥ 2 to produce ❏ . Measure the ♥ qubits. that no root exists. With high probabilit Start from uniform superposition, oly factors in cost. i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ . the unique ❏ such ❏ t
Algorithm details for unique root: Step 1: Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an advance. ❜ ❏ = ❛ ❏ otherwise. integer between 0 and 2 ♥ � 1. This is about as easy ♥ bits are enough space large. as computing Σ. to store one such integer. ✷ ❢ ❀ 1 ❣ . Step 2: “Grover diffusion”. ♥ qubits store much more, Set ❛ ✥ ❜ where a superposition over sets ❏ : ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ❏ ✼✦ ❏ � t ■ ❛ ■ . 2 ♥ complex amplitudes P This is also easy. ❏ ✐ ✷ ❏ ① ✐ ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with ✿ ♥ (i.e., Repeat steps 1 and 2 ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. subsequence Measuring these ♥ qubits ① ❀ ✿ ✿ ✿ ❀ ① ♥ t has chance ❥ ❛ ❏ ❥ 2 to produce ❏ . Measure the ♥ qubits. exists. With high probability this finds Start from uniform superposition, in cost. i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ . the unique ❏ such that Σ( ❏ ) t
Algorithm details for unique root: Step 1: Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , Represent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an ❜ ❏ = ❛ ❏ otherwise. integer between 0 and 2 ♥ � 1. This is about as easy ♥ bits are enough space as computing Σ. to store one such integer. Step 2: “Grover diffusion”. ♥ qubits store much more, Set ❛ ✥ ❜ where a superposition over sets ❏ : ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 2 ♥ complex amplitudes This is also easy. ❛ 0 ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with Repeat steps 1 and 2 ❥ ❛ 0 ❥ 2 + ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measuring these ♥ qubits has chance ❥ ❛ ❏ ❥ 2 to produce ❏ . Measure the ♥ qubits. With high probability this finds Start from uniform superposition, i.e., ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ . the unique ❏ such that Σ( ❏ ) = t .
rithm details for unique root: Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 ♥ resent ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an ❜ ❏ = ❛ ❏ otherwise. after 0 steps: integer between 0 and 2 ♥ � 1. This is about as easy 1.0 ♥ are enough space as computing Σ. re one such integer. Step 2: “Grover diffusion”. 0.5 ♥ qubits store much more, Set ❛ ✥ ❜ where erposition over sets ❏ : ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . ♥ complex amplitudes 0.0 This is also easy. ❛ ❀ ✿ ✿ ✿ ❀ ❛ 2 ♥ � 1 with Repeat steps 1 and 2 ✁ ✁ ✁ + ❥ ❛ 2 ♥ � 1 ❥ 2 = 1. ❥ ❛ ❥ −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measuring these ♥ qubits chance ❥ ❛ ❏ ❥ 2 to produce ❏ . Measure the ♥ qubits. −1.0 With high probability this finds from uniform superposition, ❛ ❏ = 1 ❂ 2 ♥❂ 2 for all ❏ . the unique ❏ such that Σ( ❏ ) = t .
ls for unique root: Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example ♥ ❏ ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an ❜ ❏ = ❛ ❏ otherwise. after 0 steps: 0 and 2 ♥ � 1. This is about as easy 1.0 ♥ enough space as computing Σ. such integer. Step 2: “Grover diffusion”. 0.5 ♥ much more, Set ❛ ✥ ❜ where over sets ❏ : ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . ♥ 0.0 amplitudes This is also easy. ❛ ❀ ✿ ✿ ✿ ❀ ❛ ♥ � with Repeat steps 1 and 2 ❥ ❛ ♥ � 1 ❥ 2 = 1. ❥ ❛ ❥ ✁ ✁ ✁ −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. ♥ qubits ❥ ❛ ❏ ❥ to produce ❏ . Measure the ♥ qubits. −1.0 With high probability this finds rm superposition, ❂ ♥❂ for all ❏ . the unique ❏ such that Σ( ❏ ) = t . ❛ ❏
unique root: Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥ ❣ as an ❜ ❏ = ❛ ❏ otherwise. after 0 steps: ♥ � 1. This is about as easy 1.0 ♥ as computing Σ. Step 2: “Grover diffusion”. 0.5 ♥ re, Set ❛ ✥ ❜ where ❏ : ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . ♥ 0.0 This is also easy. ❛ ❀ ✿ ✿ ✿ ❀ ❛ ♥ � Repeat steps 1 and 2 ❥ ❛ ❥ ✁ ✁ ✁ ❥ ❛ ♥ � ❥ 1. −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. ♥ ❥ ❛ ❏ ❥ duce ❏ . Measure the ♥ qubits. −1.0 With high probability this finds osition, the unique ❏ such that Σ( ❏ ) = t . ❂ ♥❂ ❛ ❏ ❏ .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 0 steps: This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after Step 1: This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after Step 1 + Step 2: This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after Step 1 + Step 2 + Step 1: This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 2 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 3 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 4 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 5 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 6 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 7 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 8 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 9 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 10 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 11 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 12 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 13 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 14 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 15 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 16 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 17 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 18 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 19 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 20 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 25 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 30 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 35 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds Good moment to stop, measure. the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 40 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 45 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 50 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds Traditional stopping point. the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 60 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 70 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 80 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 90 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds the unique ❏ such that Σ( ❏ ) = t .
Step 1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❜ ❏ = � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 ❜ ❏ = ❛ ❏ otherwise. after 100 ✂ (Step 1 + Step 2): This is about as easy 1.0 as computing Σ. Step 2: “Grover diffusion”. 0.5 Set ❛ ✥ ❜ where ❜ ❏ = � ❛ ❏ + (2 ❂ 2 ♥ ) P ■ ❛ ■ . 0.0 This is also easy. Repeat steps 1 and 2 −0.5 about 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. Measure the ♥ qubits. −1.0 With high probability this finds Very bad stopping point. the unique ❏ such that Σ( ❏ ) = t .
1: Set ❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ ❜ ❏ � ❛ ❏ if Σ( ❏ ) = t , for 36634 example with ♥ = 12 by a vecto ❜ ❏ ❛ ❏ otherwise. after 100 ✂ (Step 1 + Step 2): (with fixed about as easy (1) ❛ ❏ fo ❏ 1.0 computing Σ. (2) ❛ ❏ fo ❏ 2: “Grover diffusion”. Step 1 + 0.5 ❛ ✥ ❜ where act linea � ❛ ❏ + (2 ❂ 2 ♥ ) P ❜ ❏ ■ ❛ ■ . Easily compute 0.0 also easy. and pow eat steps 1 and 2 to understand −0.5 0 ✿ 58 ✁ 2 0 ✿ 5 ♥ times. of state ✮ Probabilit ✙ Measure the ♥ qubits. ✿ ♥ after ✙ ( ✙❂ −1.0 high probability this finds Very bad stopping point. unique ❏ such that Σ( ❏ ) = t .
❛ ✥ ❜ where Graph of ❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ is completely ❜ ❏ � ❛ ❏ ❏ ) = t , for 36634 example with ♥ = 12 by a vector of two ❜ ❏ ❛ ❏ otherwise. after 100 ✂ (Step 1 + Step 2): (with fixed multiplicities): easy (1) ❛ ❏ for roots ❏ ; 1.0 Σ. (2) ❛ ❏ for non-roots ❏ diffusion”. Step 1 + Step 2 0.5 ❛ ✥ ❜ act linearly on this ❂ 2 ♥ ) P ❜ ❏ � ❛ ❏ ■ ❛ ■ . Easily compute eigenvalues 0.0 . and powers of this and 2 to understand evolution −0.5 ✿ ♥ times. ✿ ✁ of state of Grover’s ✮ Probability is ✙ ♥ qubits. after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ −1.0 robability this finds Very bad stopping point. ❏ such that Σ( ❏ ) = t .
❛ ✥ ❜ Graph of ❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ is completely describ ❜ ❏ � ❛ ❏ ❏ t for 36634 example with ♥ = 12 by a vector of two numbers ❜ ❏ ❛ ❏ after 100 ✂ (Step 1 + Step 2): (with fixed multiplicities): (1) ❛ ❏ for roots ❏ ; 1.0 (2) ❛ ❏ for non-roots ❏ . diffusion”. Step 1 + Step 2 0.5 ❛ ✥ ❜ act linearly on this vector. ❂ ♥ P ❜ ❏ � ❛ ❏ ■ ❛ ■ . Easily compute eigenvalues 0.0 and powers of this linear map to understand evolution −0.5 ✿ ♥ ✿ ✁ of state of Grover’s algorithm. ✮ Probability is ✙ 1 ♥ after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations. −1.0 finds Very bad stopping point. ❏ ❏ ) = t .
Graph of ❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ is completely described for 36634 example with ♥ = 12 by a vector of two numbers after 100 ✂ (Step 1 + Step 2): (with fixed multiplicities): (1) ❛ ❏ for roots ❏ ; 1.0 (2) ❛ ❏ for non-roots ❏ . Step 1 + Step 2 0.5 act linearly on this vector. Easily compute eigenvalues 0.0 and powers of this linear map to understand evolution −0.5 of state of Grover’s algorithm. ✮ Probability is ✙ 1 after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations. −1.0 Very bad stopping point.
of ❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ is completely described Left-right 36634 example with ♥ = 12 by a vector of two numbers Don’t need 100 ✂ (Step 1 + Step 2): (with fixed multiplicities): to achieve ✿ (1) ❛ ❏ for roots ❏ ; For simplicit ♥ ✷ (2) ❛ ❏ for non-roots ❏ . 1974 Ho Step 1 + Step 2 Sort list ❏ act linearly on this vector. for all ❏ 1 ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ Easily compute eigenvalues and list of t � ❏ and powers of this linear map for all ❏ 2 ✒ ❢ ♥❂ ❀ ✿ ✿ ✿ ❀ ♥ ❣ to understand evolution Merge to of state of Grover’s algorithm. Σ( ❏ 1 ) = t � ❏ ✮ Probability is ✙ 1 i.e., Σ( ❏ 1 ❬ ❏ t after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations. bad stopping point.
❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ is completely described Left-right split (0.5) example with ♥ = 12 by a vector of two numbers Don’t need quantum ✂ (Step 1 + Step 2): (with fixed multiplicities): to achieve exponent ✿ (1) ❛ ❏ for roots ❏ ; For simplicity assume ♥ ✷ (2) ❛ ❏ for non-roots ❏ . 1974 Horowitz–Sahni: Step 1 + Step 2 Sort list of Σ( ❏ 1 ) act linearly on this vector. for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ Easily compute eigenvalues and list of t � Σ( ❏ and powers of this linear map for all ❏ 2 ✒ ❢ ♥❂ 2 + ❀ ✿ ✿ ✿ ❀ ♥ ❣ to understand evolution Merge to find collisions of state of Grover’s algorithm. Σ( ❏ 1 ) = t � Σ( ❏ 2 ), ✮ Probability is ✙ 1 i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations. stopping point.
❏ ✼✦ ❛ ❏ ❏ ✼✦ ❛ ❏ is completely described Left-right split (0.5) ♥ = 12 by a vector of two numbers Don’t need quantum computers ✂ 2): (with fixed multiplicities): to achieve exponent 0 ✿ 5. (1) ❛ ❏ for roots ❏ ; For simplicity assume ♥ ✷ 2 Z (2) ❛ ❏ for non-roots ❏ . 1974 Horowitz–Sahni: Step 1 + Step 2 Sort list of Σ( ❏ 1 ) act linearly on this vector. for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ Easily compute eigenvalues and list of t � Σ( ❏ 2 ) and powers of this linear map for all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ to understand evolution Merge to find collisions of state of Grover’s algorithm. Σ( ❏ 1 ) = t � Σ( ❏ 2 ), ✮ Probability is ✙ 1 i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations.
❏ ✼✦ ❛ ❏ is completely described Left-right split (0.5) by a vector of two numbers Don’t need quantum computers (with fixed multiplicities): to achieve exponent 0 ✿ 5. (1) ❛ ❏ for roots ❏ ; For simplicity assume ♥ ✷ 2 Z . (2) ❛ ❏ for non-roots ❏ . 1974 Horowitz–Sahni: Step 1 + Step 2 Sort list of Σ( ❏ 1 ) act linearly on this vector. for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ Easily compute eigenvalues and list of t � Σ( ❏ 2 ) and powers of this linear map for all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . to understand evolution Merge to find collisions of state of Grover’s algorithm. Σ( ❏ 1 ) = t � Σ( ❏ 2 ), ✮ Probability is ✙ 1 i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . after ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations.
Cost 2 0 ✿ 5 ♥ ❏ ✼✦ ❛ ❏ is completely described Left-right split (0.5) vector of two numbers We assign Don’t need quantum computers fixed multiplicities): to achieve exponent 0 ✿ 5. e.g. 36634 ❛ ❏ for roots ❏ ; (499 ❀ 852 ❀ ❀ ❀ ❀ ❀ For simplicity assume ♥ ✷ 2 Z . ❛ ❏ for non-roots ❏ . 4688 ❀ 5989 ❀ ❀ ❀ ❀ 1974 Horowitz–Sahni: + Step 2 Sort the Sort list of Σ( ❏ 1 ) linearly on this vector. 0 ❀ 499 ❀ 852 ❀ ❀ ✿ ✿ ✿ ❀ for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ compute eigenvalues 499 + 852 ✁ ✁ ✁ and list of t � Σ( ❏ 2 ) wers of this linear map and the for all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . understand evolution 36634 � ❀ � ❀ ✿ ✿ ✿ ❀ Merge to find collisions state of Grover’s algorithm. 36634 � � ✁ ✁ ✁ � Σ( ❏ 1 ) = t � Σ( ❏ 2 ), ✮ Probability is ✙ 1 to see that i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . ✙ ( ✙❂ 4)2 0 ✿ 5 ♥ iterations. 499 + 852 36634 � 5989 � � �
Cost 2 0 ✿ 5 ♥ for sorting, ❏ ✼✦ ❛ ❏ completely described Left-right split (0.5) o numbers We assign cost 1 to Don’t need quantum computers multiplicities): to achieve exponent 0 ✿ 5. e.g. 36634 as sum ❛ ❏ ❏ ; (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ ❀ ❀ For simplicity assume ♥ ✷ 2 Z . ❛ ❏ non-roots ❏ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ ❀ 1974 Horowitz–Sahni: Sort the 64 sums Sort list of Σ( ❏ 1 ) this vector. 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ eigenvalues 499 + 852 + 1927 ✁ ✁ ✁ and list of t � Σ( ❏ 2 ) this linear map and the 64 differences for all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . evolution 36634 � 0 ❀ 36634 � ❀ ✿ ✿ ✿ ❀ Merge to find collisions Grover’s algorithm. 36634 � 4688 � ✁ ✁ ✁ � Σ( ❏ 1 ) = t � Σ( ❏ 2 ), ✙ 1 to see that ✮ i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . ✿ ♥ iterations. ✙ ✙❂ 499 + 852 + 2535 36634 � 5989 � 6385 � �
Cost 2 0 ✿ 5 ♥ for sorting, merging. ❏ ✼✦ ❛ ❏ described Left-right split (0.5) ers We assign cost 1 to RAM. Don’t need quantum computers to achieve exponent 0 ✿ 5. e.g. 36634 as sum of ❛ ❏ ❏ (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ For simplicity assume ♥ ✷ 2 Z . ❛ ❏ ❏ 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 1974 Horowitz–Sahni: Sort the 64 sums Sort list of Σ( ❏ 1 ) 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ eigenvalues 499 + 852 + 1927 + ✁ ✁ ✁ + 3608 and list of t � Σ( ❏ 2 ) map and the 64 differences for all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . 36634 � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ Merge to find collisions rithm. 36634 � 4688 � ✁ ✁ ✁ � 9413 Σ( ❏ 1 ) = t � Σ( ❏ 2 ), to see that ✮ ✙ i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . ✿ ♥ iterations. ✙ ✙❂ 499 + 852 + 2535 + 3608 = 36634 � 5989 � 6385 � 7353 �
Cost 2 0 ✿ 5 ♥ for sorting, merging. Left-right split (0.5) We assign cost 1 to RAM. Don’t need quantum computers to achieve exponent 0 ✿ 5. e.g. 36634 as sum of (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ For simplicity assume ♥ ✷ 2 Z . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): 1974 Horowitz–Sahni: Sort the 64 sums Sort list of Σ( ❏ 1 ) 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ for all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ 499 + 852 + 1927 + ✁ ✁ ✁ + 3608 and list of t � Σ( ❏ 2 ) and the 64 differences for all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . 36634 � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ Merge to find collisions 36634 � 4688 � ✁ ✁ ✁ � 9413 Σ( ❏ 1 ) = t � Σ( ❏ 2 ), to see that i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . 499 + 852 + 2535 + 3608 = 36634 � 5989 � 6385 � 7353 � 9413.
Cost 2 0 ✿ 5 ♥ for sorting, merging. Left-right split (0.5) Moduli (0.5) We assign cost 1 to RAM. need quantum computers For simplicit ♥ ✷ achieve exponent 0 ✿ 5. e.g. 36634 as sum of ✿ ♥ Choose ▼ ✙ (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ simplicity assume ♥ ✷ 2 Z . Choose t ✷ ❢ ❀ ❀ ✿ ✿ ✿ ❀ ▼ � ❣ 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): Define t 2 t � t Horowitz–Sahni: Sort the 64 sums ist of Σ( ❏ 1 ) Find all ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ such that ❏ ✑ t ▼ 499 + 852 + 1927 + ✁ ✁ ✁ + 3608 list of t � Σ( ❏ 2 ) How? Split ❏ ❏ ❬ ❏ and the 64 differences ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . Find all ❏ ✒ ❢ ♥❂ ❀ ✿ ✿ ✿ ❀ ♥ ❣ 36634 � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ to find collisions such that ❏ ✑ t ▼ 36634 � 4688 � ✁ ✁ ✁ � 9413 ❏ = t � Σ( ❏ 2 ), to see that Sort and Σ( ❏ 1 ❬ ❏ 2 ) = t . 499 + 852 + 2535 + 3608 = collisions ❏ t � ❏ 36634 � 5989 � 6385 � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ t
Cost 2 0 ✿ 5 ♥ for sorting, merging. (0.5) Moduli (0.5) We assign cost 1 to RAM. quantum computers For simplicity assume ♥ ✷ ent 0 ✿ 5. e.g. 36634 as sum of Choose ▼ ✙ 2 0 ✿ 25 ♥ (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ assume ♥ ✷ 2 Z . Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � ❣ 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): Define t 2 = t � t 1 . witz–Sahni: Sort the 64 sums ❏ ) Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ such that Σ( ❏ 1 ) ✑ t ▼ 499 + 852 + 1927 + ✁ ✁ ✁ + 3608 t � Σ( ❏ 2 ) How? Split ❏ 1 as ❏ ❬ ❏ and the 64 differences ❏ ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . Find all ❏ 2 ✒ ❢ ♥❂ 2 ❀ ✿ ✿ ✿ ❀ ♥ ❣ 36634 � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ collisions such that Σ( ❏ 2 ) ✑ t ▼ 36634 � 4688 � ✁ ✁ ✁ � 9413 ❏ t � ❏ 2 ), to see that Sort and merge to ❏ ❬ ❏ = t . 499 + 852 + 2535 + 3608 = collisions Σ( ❏ 1 ) = t � ❏ 36634 � 5989 � 6385 � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t
Cost 2 0 ✿ 5 ♥ for sorting, merging. Moduli (0.5) We assign cost 1 to RAM. computers For simplicity assume ♥ ✷ 4 Z ✿ e.g. 36634 as sum of Choose ▼ ✙ 2 0 ✿ 25 ♥ . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ ♥ ✷ 2 Z . Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): Define t 2 = t � t 1 . Sort the 64 sums ❏ Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ 499 + 852 + 1927 + ✁ ✁ ✁ + 3608 t � ❏ How? Split ❏ 1 as ❏ 11 ❬ ❏ 12 . and the 64 differences ❏ ✒ ❢ ♥❂ ❀ ✿ ✿ ✿ ❀ ♥ ❣ . Find all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ 36634 � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 2 ) ✑ t 2 (mod ▼ 36634 � 4688 � ✁ ✁ ✁ � 9413 ❏ t � ❏ to see that Sort and merge to find all ❏ ❬ ❏ t 499 + 852 + 2535 + 3608 = collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), 36634 � 5989 � 6385 � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t .
Cost 2 0 ✿ 5 ♥ for sorting, merging. Moduli (0.5) We assign cost 1 to RAM. For simplicity assume ♥ ✷ 4 Z . e.g. 36634 as sum of Choose ▼ ✙ 2 0 ✿ 25 ♥ . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): Define t 2 = t � t 1 . Sort the 64 sums Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ 0 ❀ 499 ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ). 499 + 852 + 1927 + ✁ ✁ ✁ + 3608 How? Split ❏ 1 as ❏ 11 ❬ ❏ 12 . and the 64 differences Find all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ 36634 � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 2 ) ✑ t 2 (mod ▼ ). 36634 � 4688 � ✁ ✁ ✁ � 9413 to see that Sort and merge to find all 499 + 852 + 2535 + 3608 = collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), 36634 � 5989 � 6385 � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t .
0 ✿ 5 ♥ for sorting, merging. Moduli (0.5) Finds ❏ iff ❏ ✑ t ✿ ♥ assign cost 1 to RAM. There ar ✙ t For simplicity assume ♥ ✷ 4 Z . ✿ ♥ Each choice 36634 as sum of Choose ▼ ✙ 2 0 ✿ 25 ♥ . ✿ ♥ Total cost ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): Not visible Define t 2 = t � t 1 . ✿ ♥ this uses e 64 sums Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ assuming ❀ ❀ 852 ❀ 499 + 852 ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ). 852 + 1927 + ✁ ✁ ✁ + 3608 Algorithm How? Split ❏ 1 as ❏ 11 ❬ ❏ 12 . the 64 differences introduced Find all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ � 0 ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ 2006 Elsenhans–Jahnel; such that Σ( ❏ 2 ) ✑ t 2 (mod ▼ ). � 4688 � ✁ ✁ ✁ � 9413 2010 Ho that Sort and merge to find all Different 852 + 2535 + 3608 = collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), for simila � 5989 � 6385 � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . 1981 Schro
✿ ♥ rting, merging. Moduli (0.5) Finds ❏ iff Σ( ❏ 1 ) ✑ t There are ✙ 2 0 ✿ 25 ♥ to RAM. t For simplicity assume ♥ ✷ 4 Z . ✿ ♥ Each choice costs sum of Choose ▼ ✙ 2 0 ✿ 25 ♥ . Total cost 2 0 ✿ 5 ♥ . ❀ ❀ ❀ 2535 ❀ 3596 ❀ 3608 ❀ Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . ❀ ❀ ❀ 7353 ❀ 7650 ❀ 9413): Not visible in cost Define t 2 = t � t 1 . ✿ ♥ this uses space only sums Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ assuming typical distribution. ❀ ❀ ❀ 852 ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ). 1927 + ✁ ✁ ✁ + 3608 Algorithm has been How? Split ❏ 1 as ❏ 11 ❬ ❏ 12 . differences introduced at least Find all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ � ❀ 36634 � 4688 ❀ ✿ ✿ ✿ ❀ 2006 Elsenhans–Jahnel; such that Σ( ❏ 2 ) ✑ t 2 (mod ▼ ). � � ✁ ✁ ✁ � 9413 2010 Howgrave-Graha Sort and merge to find all Different technique 2535 + 3608 = collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), for similar space reduction: � � 6385 � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . 1981 Schroeppel–Shamir.
✿ ♥ merging. Moduli (0.5) Finds ❏ iff Σ( ❏ 1 ) ✑ t 1 . There are ✙ 2 0 ✿ 25 ♥ choices of t RAM. For simplicity assume ♥ ✷ 4 Z . Each choice costs 2 0 ✿ 25 ♥ . Choose ▼ ✙ 2 0 ✿ 25 ♥ . Total cost 2 0 ✿ 5 ♥ . ❀ ❀ ❀ ❀ ❀ 3608 ❀ Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . ❀ ❀ ❀ ❀ 7650 ❀ 9413): Not visible in cost metric: Define t 2 = t � t 1 . this uses space only 2 0 ✿ 25 ♥ , Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ assuming typical distribution. ❀ ❀ ❀ ❀ ✿ ✿ ✿ ❀ such that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ). ✁ ✁ ✁ 3608 Algorithm has been How? Split ❏ 1 as ❏ 11 ❬ ❏ 12 . introduced at least twice: Find all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ � ❀ � ❀ ✿ ✿ ✿ ❀ 2006 Elsenhans–Jahnel; such that Σ( ❏ 2 ) ✑ t 2 (mod ▼ ). � � ✁ ✁ ✁ � 9413 2010 Howgrave-Graham–Joux. Sort and merge to find all Different technique = collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), for similar space reduction: � � � 7353 � 9413. i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . 1981 Schroeppel–Shamir.
Moduli (0.5) Finds ❏ iff Σ( ❏ 1 ) ✑ t 1 . There are ✙ 2 0 ✿ 25 ♥ choices of t 1 . For simplicity assume ♥ ✷ 4 Z . Each choice costs 2 0 ✿ 25 ♥ . Choose ▼ ✙ 2 0 ✿ 25 ♥ . Total cost 2 0 ✿ 5 ♥ . Choose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . Not visible in cost metric: Define t 2 = t � t 1 . this uses space only 2 0 ✿ 25 ♥ , Find all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ assuming typical distribution. such that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ). Algorithm has been How? Split ❏ 1 as ❏ 11 ❬ ❏ 12 . introduced at least twice: Find all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ 2006 Elsenhans–Jahnel; such that Σ( ❏ 2 ) ✑ t 2 (mod ▼ ). 2010 Howgrave-Graham–Joux. Sort and merge to find all Different technique collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), for similar space reduction: i.e., Σ( ❏ 1 ❬ ❏ 2 ) = t . 1981 Schroeppel–Shamir.
duli (0.5) Finds ❏ iff Σ( ❏ 1 ) ✑ t 1 . e.g. ▼ = t ① There are ✙ 2 0 ✿ 25 ♥ choices of t 1 . (499 ❀ 852 ❀ ❀ ❀ ❀ ❀ simplicity assume ♥ ✷ 4 Z . Each choice costs 2 0 ✿ 25 ♥ . 4688 ❀ 5989 ❀ ❀ ❀ ❀ ose ▼ ✙ 2 0 ✿ 25 ♥ . Total cost 2 0 ✿ 5 ♥ . Try each t ✷ ❢ ❀ ❀ ✿ ✿ ✿ ❀ ❣ ose t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . Not visible in cost metric: t 2 = t � t 1 . In particula t this uses space only 2 0 ✿ 25 ♥ , There are all ❏ 1 ✒ ❢ 1 ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ assuming typical distribution. (499 ❀ 852 ❀ ❀ ❀ ❀ that Σ( ❏ 1 ) ✑ t 1 (mod ▼ ). Algorithm has been with sum Split ❏ 1 as ❏ 11 ❬ ❏ 12 . introduced at least twice: There are all ❏ 2 ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ 2006 Elsenhans–Jahnel; (4688 ❀ 5989 ❀ ❀ ❀ ❀ that Σ( ❏ 2 ) ✑ t 2 (mod ▼ ). 2010 Howgrave-Graham–Joux. with sum � Sort and and merge to find all Different technique 499 + 852 collisions Σ( ❏ 1 ) = t � Σ( ❏ 2 ), for similar space reduction: 36634 � 5989 � � � Σ( ❏ 1 ❬ ❏ 2 ) = t . 1981 Schroeppel–Shamir.
Finds ❏ iff Σ( ❏ 1 ) ✑ t 1 . e.g. ▼ = 8, t = 36634, ① There are ✙ 2 0 ✿ 25 ♥ choices of t 1 . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ ❀ ❀ assume ♥ ✷ 4 Z . Each choice costs 2 0 ✿ 25 ♥ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ ❀ ✿ 25 ♥ . Total cost 2 0 ✿ 5 ♥ . ▼ ✙ Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ ❣ t ✷ ❢ ❀ 1 ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . Not visible in cost metric: t t � t 1 . In particular try t 1 this uses space only 2 0 ✿ 25 ♥ , There are 12 subsequences ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥❂ 2 ❣ assuming typical distribution. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ ❀ (mod ▼ ). ❏ ✑ t 1 Algorithm has been with sum 6 modulo ❏ as ❏ 11 ❬ ❏ 12 . introduced at least twice: There are 6 subsequences ❏ ✒ ❢ ♥❂ 2 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ 2006 Elsenhans–Jahnel; (4688 ❀ 5989 ❀ 6385 ❀ ❀ ❀ (mod ▼ ). ❏ ✑ t 2 2010 Howgrave-Graham–Joux. with sum 36634 � Sort and merge to to find all Different technique 499 + 852 + 2535 ❏ = t � Σ( ❏ 2 ), for similar space reduction: 36634 � 5989 � 6385 � � = t . ❏ ❬ ❏ 1981 Schroeppel–Shamir.
Finds ❏ iff Σ( ❏ 1 ) ✑ t 1 . e.g. ▼ = 8, t = 36634, ① = There are ✙ 2 0 ✿ 25 ♥ choices of t 1 . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ ♥ ✷ 4 Z . Each choice costs 2 0 ✿ 25 ♥ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ ✿ ♥ Total cost 2 0 ✿ 5 ♥ . ▼ ✙ Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ 7 ❣ . t ✷ ❢ ❀ ❀ ✿ ✿ ✿ ❀ ▼ � 1 ❣ . Not visible in cost metric: t t � t In particular try t 1 = 6. this uses space only 2 0 ✿ 25 ♥ , There are 12 subsequences of ❏ ✒ ❢ ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ assuming typical distribution. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608) (mod ▼ ). ❏ ✑ t Algorithm has been with sum 6 modulo 8. ❏ ❏ ❬ ❏ . introduced at least twice: There are 6 subsequences of ❏ ✒ ❢ ♥❂ ❀ ✿ ✿ ✿ ❀ ♥ ❣ 2006 Elsenhans–Jahnel; (4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ (mod ▼ ). ❏ ✑ t 2010 Howgrave-Graham–Joux. with sum 36634 � 6 modulo Sort and merge to find Different technique 499 + 852 + 2535 + 3608 = ❏ t � ❏ 2 ), for similar space reduction: 36634 � 5989 � 6385 � 7353 � ❏ ❬ ❏ t 1981 Schroeppel–Shamir.
Finds ❏ iff Σ( ❏ 1 ) ✑ t 1 . e.g. ▼ = 8, t = 36634, ① = There are ✙ 2 0 ✿ 25 ♥ choices of t 1 . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Each choice costs 2 0 ✿ 25 ♥ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): Total cost 2 0 ✿ 5 ♥ . Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ 7 ❣ . Not visible in cost metric: In particular try t 1 = 6. this uses space only 2 0 ✿ 25 ♥ , There are 12 subsequences of assuming typical distribution. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608) Algorithm has been with sum 6 modulo 8. introduced at least twice: There are 6 subsequences of 2006 Elsenhans–Jahnel; (4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) 2010 Howgrave-Graham–Joux. with sum 36634 � 6 modulo 8. Sort and merge to find Different technique 499 + 852 + 2535 + 3608 = for similar space reduction: 36634 � 5989 � 6385 � 7353 � 9413. 1981 Schroeppel–Shamir.
❏ iff Σ( ❏ 1 ) ✑ t 1 . e.g. ▼ = 8, t = 36634, ① = Quantum ✿ ✿ ✿ ✿ are ✙ 2 0 ✿ 25 ♥ choices of t 1 . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Cost 2 ♥❂ choice costs 2 0 ✿ 25 ♥ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): 1998 Brassa cost 2 0 ✿ 5 ♥ . Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ 7 ❣ . For simplicit ♥ ✷ visible in cost metric: In particular try t 1 = 6. uses space only 2 0 ✿ 25 ♥ , Compute ❏ There are 12 subsequences of ❏ 1 ✒ ❢ 1 ❀ ❀ ✿ ✿ ✿ ❀ ♥❂ ❣ assuming typical distribution. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608) Sort ▲ = ❢ ❏ ❣ rithm has been with sum 6 modulo 8. Can now duced at least twice: There are 6 subsequences of ❏ 2 ✼✦ [ t � ❏ ✷ ▲ ❂ Elsenhans–Jahnel; (4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) for ❏ 2 ✒ ❢ ♥❂ ❀ ✿ ✿ ✿ ❀ ♥ ❣ Howgrave-Graham–Joux. with sum 36634 � 6 modulo 8. Recall: w Sort and merge to find Different technique 499 + 852 + 2535 + 3608 = Use Grover’s imilar space reduction: 36634 � 5989 � 6385 � 7353 � 9413. whether Schroeppel–Shamir.
❏ ❏ ) ✑ t 1 . e.g. ▼ = 8, t = 36634, ① = Quantum left-right ✿ ✿ ✿ ✿ ♥ choices of t 1 . ✿ ✙ (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Cost 2 ♥❂ 3 , imitating costs 2 0 ✿ 25 ♥ . 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): 1998 Brassard–Høy ✿ ♥ . Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ 7 ❣ . For simplicity assume ♥ ✷ cost metric: In particular try t 1 = 6. only 2 0 ✿ 25 ♥ , Compute Σ( ❏ 1 ) fo There are 12 subsequences of ❏ 1 ✒ ❢ 1 ❀ 2 ❀ ✿ ✿ ✿ ❀ ♥❂ 3 ❣ ypical distribution. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608) Sort ▲ = ❢ Σ( ❏ 1 ) ❣ . een with sum 6 modulo 8. Can now efficiently least twice: There are 6 subsequences of ❏ 2 ✼✦ [ t � Σ( ❏ 2 ) ❂ ✷ ▲ Elsenhans–Jahnel; (4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) for ❏ 2 ✒ ❢ ♥❂ 3 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ wgrave-Graham–Joux. with sum 36634 � 6 modulo 8. Recall: we assign cost Sort and merge to find nique 499 + 852 + 2535 + 3608 = Use Grover’s metho reduction: 36634 � 5989 � 6385 � 7353 � 9413. whether this function el–Shamir.
❏ ❏ ✑ t e.g. ▼ = 8, t = 36634, ① = Quantum left-right split (0 ✿ 333 ✿ ✿ ✿ ✿ ♥ ✙ of t 1 . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Cost 2 ♥❂ 3 , imitating ✿ ♥ 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): 1998 Brassard–Høyer–Tapp: ✿ ♥ Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ 7 ❣ . For simplicity assume ♥ ✷ 3 Z In particular try t 1 = 6. ✿ ♥ , Compute Σ( ❏ 1 ) for all There are 12 subsequences of ❏ 1 ✒ ❢ 1 ❀ 2 ❀ ✿ ✿ ✿ ❀ ♥❂ 3 ❣ . distribution. (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608) Sort ▲ = ❢ Σ( ❏ 1 ) ❣ . with sum 6 modulo 8. Can now efficiently compute There are 6 subsequences of ❏ 2 ✼✦ [ t � Σ( ❏ 2 ) ❂ ✷ ▲ ] (4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) for ❏ 2 ✒ ❢ ♥❂ 3 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . –Joux. with sum 36634 � 6 modulo 8. Recall: we assign cost 1 to RAM. Sort and merge to find 499 + 852 + 2535 + 3608 = Use Grover’s method to see reduction: 36634 � 5989 � 6385 � 7353 � 9413. whether this function has a ro
e.g. ▼ = 8, t = 36634, ① = Quantum left-right split (0 ✿ 333 ✿ ✿ ✿ ) (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608 ❀ Cost 2 ♥❂ 3 , imitating 4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413): 1998 Brassard–Høyer–Tapp: Try each t 1 ✷ ❢ 0 ❀ 1 ❀ ✿ ✿ ✿ ❀ 7 ❣ . For simplicity assume ♥ ✷ 3 Z . In particular try t 1 = 6. Compute Σ( ❏ 1 ) for all There are 12 subsequences of ❏ 1 ✒ ❢ 1 ❀ 2 ❀ ✿ ✿ ✿ ❀ ♥❂ 3 ❣ . (499 ❀ 852 ❀ 1927 ❀ 2535 ❀ 3596 ❀ 3608) Sort ▲ = ❢ Σ( ❏ 1 ) ❣ . with sum 6 modulo 8. Can now efficiently compute There are 6 subsequences of ❏ 2 ✼✦ [ t � Σ( ❏ 2 ) ❂ ✷ ▲ ] (4688 ❀ 5989 ❀ 6385 ❀ 7353 ❀ 7650 ❀ 9413) for ❏ 2 ✒ ❢ ♥❂ 3 + 1 ❀ ✿ ✿ ✿ ❀ ♥ ❣ . with sum 36634 � 6 modulo 8. Recall: we assign cost 1 to RAM. Sort and merge to find 499 + 852 + 2535 + 3608 = Use Grover’s method to see 36634 � 5989 � 6385 � 7353 � 9413. whether this function has a root.
Recommend
More recommend
