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Acta Scientiarum Naturalium Universitatis Sunyatseni

Transform of Quantum States Via Adiabatic Quantum Evolution

Shi‐Dong Liang

Published Jan 1, 2007• 0 citations
OpenAlex

Abstract

The success of quantum algorithms is to implement correctly quantum states transform,which is mainly achieved by suitable quantum operators.It is shown that the finding of a suitable quantum operator is a great challenge.Most of previous studies solve this problem by machine learning methods.However,these algorithms are different from the standard circuit model of quantum computation which characterizes as unitary quantum operations,thus they are difficult to be realized on quantum computers.It is shown that the transform of quantum states can be implemented by adiabatic quantum evolution,which is a kind of pure quantum effect and suitable well for physical implementation.The adiabatic theorem guarantees that the final state of the system would be the desired output.It is therefore a remarkable approach for quantum states transform in quantum computation and quantum communication.

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Bulletin of the American Physical Society•Mar 18, 2013•0 citations
The success of quantum algorithms is to implement correctly quantum states transform,which is mainly achieved by suitable quantum operators.It is shown that the finding of a suitable quantum operator is a great challenge.Most of previous studies solve this problem by machine learning methods.However,these algorithms are different from the standard circuit model of quantum computation which characterizes as unitary quantum operations,thus they are difficult to be realized on quantum computers.It is shown that the transform of quantum states can be implemented by adiabatic quantum evolution,which is a kind of pure quantum effect and suitable well for physical implementation.The adiabatic theorem guarantees that the final state of the system would be the desired output.It is therefore a remarkable approach for quantum states transform in quantum computation and quantum communication.
We give a method of mapping any arbitrary circuit in the standard quantum computing model to an adiabatic algorithm of the same depth. It is a direct mapping as we use the same initial, intermediate and final states as in the circuit. Hence we show constructively the existence of efficient adiabatic evolution paths for a class of problems. Since the physical implementation of unitary gates in the circuit model requires precise timing control, the equivalent adiabatic construction may also provide a more realizable alternative.
The design of new quantum algorithms has proven to be an extremely difficult task. This paper considers a different approach to the problem, by studying the problem of 'quantum state generation'. This approach provides intriguing links between many different areas: quantum computation, adiabatic evolution, analysis of spectral gaps and groundstates of Hamiltonians, rapidly mixing Markov chains, the complexity class statistical zero knowledge, quantum random walks, and more. We first show that many natural candidates for quantum algorithms can be cast as a state generation problem. We define a paradigm for state generation, called 'adiabatic state generation' and develop tools for adiabatic state generation which include methods for implementing very general Hamiltonians and ways to guarantee non negligible spectral gaps. We use our tools to prove that adiabatic state generation is equivalent to state generation in the standard quantum computing model, and finally we show how to apply our techniques to generate interesting superpositions related to Markov chains.
The design of new quantum algorithms has proven to be an extremely difficult task. This paper considers a different approach to the problem, by studying the problem of 'quantum state generation'. This approach provides intriguing links between many different areas: quantum computation, adiabatic evolution, analysis of spectral gaps and groundstates of Hamiltonians, rapidly mixing Markov chains, the complexity class statistical zero knowledge, quantum random walks, and more. We first show that many natural candidates for quantum algorithms can be cast as a state generation problem. We define a paradigm for state generation, called 'adiabatic state generation' and develop tools for adiabatic state generation which include methods for implementing very general Hamiltonians and ways to guarantee non negligible spectral gaps. We use our tools to prove that adiabatic state generation is equivalent to state generation in the standard quantum computing model, and finally we show how to apply our techniques to generate interesting superpositions related to Markov chains.
In adiabatic quantum computing the aim is to track an eigenstate as the Hamiltonian changes. In the usual setup this is achieved using the natural time-dependent Hamiltonian evolution of the system and the main technical tool is the adiabatic theorem. We propose several alternative processes that achieve the same goal, but can easily be implemented on a gate-based quantum computer without the overhead of simulating time-dependent Hamiltonian evolution. We give a general framework for deriving `adiabatic' theorems for these processes. As an application, we give various algorithms for solving the Quantum Linear Systems Problem (QLSP) with optimal scaling in the condition number. One of these algorithms was previously developed in [Cunningham, Roland 2024] and another can be seen as a randomised version of the discrete adiabatic algorithm of [Costa et al. 2022]. We also describe versions of Trotterisation in our framework, which allows several results from [An et al. 2025] to be reproduced in a randomised setting. In particular, bounds on the Trotter error in terms of the fidelity are obtained that are asymptotically better than the standard bounds.
While quantum computers are naturally well-suited to implementing linear operations, it is less clear how to implement nonlinear operations on quantum computers. However, nonlinear subroutines may prove key to a range of applications of quantum computing from solving nonlinear equations to data processing and quantum machine learning. Here we develop a series of basic subroutines for implementing nonlinear transformations of input quantum states. Our algorithms are framed around the concept of a weighted state, a mathematical entity describing the output of an operational procedure involving both quantum circuits and classical post-processing.
Many problems in science could be solved by preparing the low-energy quantum state (or any eigenstate) of a Hamiltonian. A common example is the Boolean satisfiability problem, where each clause can be mapped to the energy of an interacting many-body system, and the problem reduces to minimizing the energy. In quantum computing, adiabatic quantum state transformations (ASTs) provide a tool for preparing the quantum state. ASTs are conventionally implemented via slow or adiabatic perturbations to the Hamiltonian, relying on the quantum adiabatic theorem. Nevertheless, more efficient implementations of ASTs exist. In this talk I will review recently developed methods for ASTs that are more efficient and require less assumptions on the Hamiltonians than the conventional implementation [1,2]. Such methods involve measurements of the states along the evolution path and have a best-case implementation cost of L/G, where L is the length of the (evolved) state path and G is a lower bound to the spectral gap of the Hamiltonians. I will show that this cost is optimal [3] and comment on results of the gap amplification problem, where the goal is to reduce the cost by increasing G [4].