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arXiv:quant-ph/0205059

Quantum Computing and Dynamical Quantum Models

Scott Aaronson

Published May 11, 2002• 1 citation
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Abstract

A dynamical quantum model assigns an eigenstate to a specified observable even when no measurement is made, and gives a stochastic evolution rule for that eigenstate. Such a model yields a distribution over classical histories of a quantum state. We study what can be computed by sampling from that distribution, i.e., by examining an observer's entire history. We show that, relative to an oracle, one can solve problems in polynomial time that are intractable even for quantum computers; and can search an N-element list in order N^{1/3} steps (though not fewer).

DOI
10.48550/arxiv.quant-ph/0205059

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A dynamical quantum model assigns an eigenstate to a specified observable even when no measurement is made, and gives a stochastic evolution rule for that eigenstate. Such a model yields a distribution over classical histories of a quantum state. We study what can be computed by sampling from that distribution, i.e., by examining an observer's entire history. We show that, relative to an oracle, one can solve problems in polynomial time that are intractable even for quantum computers; and can search an N-element list in order N^{1/3} steps (though not fewer).
This paper shows that, if we could examine the entire history of a hidden variable, then we could efficiently solve problems that are believed to be intractable even for quantum computers. In particular, under any hidden-variable theory satisfying a reasonable axiom called "indifference to the identity," we could solve the Graph Isomorphism and Approximate Shortest Vector problems in polynomial time, as well as an oracle problem that is known to require quantum exponential time. We could also search an N-item database using O(N^{1/3}) queries, as opposed to O(N^{1/2}) queries with Grover's search algorithm. On the other hand, the N^{1/3} bound is optimal, meaning that we could probably not solve NP-complete problems in polynomial time. We thus obtain the first good example of a model of computation that appears slightly more powerful than the quantum computing model.
We explore the space "just above" BQP by defining a complexity class PDQP (Product Dynamical Quantum Polynomial time) which is larger than BQP but does not contain NP relative to an oracle. The class is defined by imagining that quantum computers can perform measurements that do not collapse the wavefunction. This (non-physical) model of computation can efficiently solve problems such as Graph Isomorphism and Approximate Shortest Vector which are believed to be intractable for quantum computers. Furthermore, it can search an unstructured N-element list in O~\tilde OO~(N^{1/3}) time, but no faster than Ω(N^{1/4}), and hence cannot solve NP-hard problems in a black box manner. In short, this model of computation is more powerful than standard quantum computation, but only slightly so. Our work is inspired by previous work of Aaronson on the power of sampling the histories of hidden variables. However Aaronson's work contains an error in its proof of the lower bound for search, and hence it is unclear whether or not his model allows for search in logarithmic time. Our work can be viewed as a conceptual simplification of Aaronson's approach, with a provable polynomial lower bound for search.
This paper initiates the study of hidden variables from a quantum computing perspective. For us, a hidden-variable theory is simply a way to convert a unitary matrix that maps one quantum state to another into a stochastic matrix that maps the initial probability distribution to the final one in some fixed basis. We list five axioms that we might want such a theory to satisfy and then investigate which of the axioms can be satisfied simultaneously. Toward this end, we propose a new hidden-variable theory based on network flows. In a second part of the paper, we show that if we could examine the entire history of a hidden variable, then we could efficiently solve problems that are believed to be intractable even for quantum computers. In particular, under any hidden-variable theory satisfying a reasonable axiom, we could solve the graph isomorphism problem in polynomial time, and could search an NNN-item database using O(N1∕3)O({N}^{1∕3})O(N1∕3) queries, as opposed to O(N1∕2)O({N}^{1∕2})O(N1∕2) queries with Grover's search algorithm. On the other hand, the N1∕3{N}^{1∕3}N1∕3 bound is optimal, meaning that we could probably not solve NP\mathrm{NP}NP-complete problems in polynomial time. We thus obtain the first good example of a model of computation that appears slightly more powerful than the quantum computing model.
Quantum computers and their algorithm are now widely studied.We consider the prob- lem of making a superposition with an arbitrarily specified amplitudes.By modifying Grover's quantum search algorithm, we show that this can be done in time O(N)O(\sqrt{N})O(N​) with two simple operations, one is the Walsh-Hadamard transformation of $N\cross N$ -matrices where N=2nN=2^{n}N=2n , the other is phase rotation of a state.If we see this problem in the classical computation model, it corresponds to the problem of making a sample with an arbitrarily specified distribution, where its time complexity is Ω(N)\Omega(N)Ω(N) .By making a measurement on the result of our algorithm, it is possible to obtain a sample according to an arbitrarily specified probability distribution in O(N)O(\sqrt{N})O(N​) time.
Building on the work of Deutsch and Jozsa, we construct oracles relative to which (1) there is a decision problem that can be solved with certainty in worst-case polynomial time on the quantum computer, yet it cannot be solved classically in probabilistic expected polynomial time if errors are not tolerated, nor even in nondeterministic polynomial time, and (2) there is a decision problem that can be solved in exponential time on the quantum computer, which requires double exponential time on all but finitely many instances on any classical deterministic computer.
Depending on time, I'll discuss some subset of the following results. (1) There exists an oracle relative to which classical and quantum computers can solve the same approximate sampling problems in polynomial time, and yet the polynomial hierarchy is infinite. This means that any "BosonSampling-type theorem" will need to be non-relativizing. (2) Can P be separated from BQP by an efficiently-computable oracle (one in P/poly)? Yes if quantum-resistant one-way functions exist; no if P=PSPACE. The latter builds on recent results in quantum query complexity by myself and Shalev Ben-David. (3) How hard is it to approximately sample the output distribution of a random quantum circuit? One can do it (and even calculate the probabilities) in polynomial space and m^O(n) time, where m is the number of gates and n is the number of qubits. But if that's essentially optimal for estimating probabilities, then approximate sampling also requires exponential time. Joint work with Lijie Chen (Tsinghua University)