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arXiv:2104.00785

Unitarization Through Approximate Basis

Joshua Alan Cook

Published Apr 1, 2021• 0 citations
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Abstract

We introduce the problem of unitarization. Unitarization is the problem of taking $k$ input quantum circuits that produce orthogonal states from the all $0$ state, and create an output circuit implementing a unitary with its first $k$ columns as those states. That is, the output circuit takes the $k$th computational basis state to the state prepared by the $k$th input circuit. We allow the output circuit to use ancilla qubits initialized to $0$. But ancilla qubits must always be returned to $0$ for any input. The input circuits may use ancilla qubits, but we are only guaranteed the they return ancilla qubits to $0$ on the all $0$ input. The unitarization problem seems hard if the output states are neither orthogonal to or in the span of the computational basis states that need to map to them. In this work, we approximately solve this problem in the case where input circuits are given as black box oracles by probably finding an approximate basis for our states. This method may be more interesting than the application. This technique is a sort of quantum analogue of Gram-Schmidt orthogonalization for quantum states. Specifically, we find an approximate basis in polynomial time for the following parameters. Take any natural $n$, $k = O\left(\frac{\ln(n)}{\ln(\ln(n))}\right)$, and $ε= 2^{-O(\sqrt{\ln(n)})}$. Take any $k$ input quantum states, $(|ψ_i \rangle)_{i\in [k]}$, on polynomial in $n$ qubits prepared by quantum oracles, $(V_i)_{i \in [k]}$ (that we can control call and control invert). Then there is a quantum circuit with polynomial size in $n$ with access to the oracles $(V_i)_{i \in [k]}$ that with at least $1 - ε$ probability, computes at most $k$ circuits with size polynomial in $n$ and oracle access to $(V_i)_{i \in [k]}$ that $ε$ approximately computes an $ε$ approximate orthonormal basis for $(|ψ_i \rangle)_{i\in [k]}$.

DOI
10.48550/arxiv.2104.00785

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We introduce the problem of unitarization. Unitarization is the problem of taking kkk input quantum circuits that produce orthogonal states from the all 000 state, and create an output circuit implementing a unitary with its first kkk columns as those states. That is, the output circuit takes the kkkth computational basis state to the state prepared by the kkkth input circuit. We allow the output circuit to use ancilla qubits initialized to 000. But ancilla qubits must always be returned to 000 for any input. The input circuits may use ancilla qubits, but we are only guaranteed the they return ancilla qubits to 000 on the all 000 input. The unitarization problem seems hard if the output states are neither orthogonal to or in the span of the computational basis states that need to map to them. In this work, we approximately solve this problem in the case where input circuits are given as black box oracles by probably finding an approximate basis for our states. This method may be more interesting than the application. This technique is a sort of quantum analogue of Gram-Schmidt orthogonalization for quantum states. Specifically, we find an approximate basis in polynomial time for the following parameters. Take any natural nnn, k=O(ln⁡(n)ln⁡(ln⁡(n)))k = O\left(\frac{\ln(n)}{\ln(\ln(n))}\right)k=O(ln(ln(n))ln(n)​), and ε=2−O(ln⁡(n))ε= 2^{-O(\sqrt{\ln(n)})}ε=2−O(ln(n)​). Take any kkk input quantum states, (∣ψi⟩)i∈[k](|ψ_i \rangle)_{i\in [k]}(∣ψi​⟩)i∈[k]​, on polynomial in nnn qubits prepared by quantum oracles, (Vi)i∈[k](V_i)_{i \in [k]}(Vi​)i∈[k]​ (that we can control call and control invert). Then there is a quantum circuit with polynomial size in nnn with access to the oracles (Vi)i∈[k](V_i)_{i \in [k]}(Vi​)i∈[k]​ that with at least 1−ε1 - ε1−ε probability, computes at most kkk circuits with size polynomial in nnn and oracle access to (Vi)i∈[k](V_i)_{i \in [k]}(Vi​)i∈[k]​ that εεε approximately computes an εεε approximate orthonormal basis for (∣ψi⟩)i∈[k](|ψ_i \rangle)_{i\in [k]}(∣ψi​⟩)i∈[k]​.
In this paper, I take a step toward answering the following question: for m different small circuits that compute m orthogonal n qubit states, is there a small circuit that will map m computational basis states to these m states without any input leaving any auxiliary bits changed. While this may seem simple, the constraint that auxiliary bits always be returned to 0 on any input (even ones besides the m we care about) led me to use sophisticated techniques. I give an approximation of such a unitary in the m = 2 case that has size polynomial in the approximation error, and the number of qubits n.
Let H1,H2H_1, H_2H1​,H2​ be Hilbert spaces of the same finite dimension ≥2\ge2≥2, and CCC an arbitrary quantum circuit with (principal) input state in H1H_1H1​ and (principal) output state in H2H_2H2​. CCC may use ancillas and produce garbage which is traced out. CCC may employ classical channels and measurement gates. If CCC computes, for each computation path μ\muμ through the circuit, a unitary transformation Uμ:H1→H2U_\mu: H_1 \to H_2Uμ​:H1​→H2​ then, for each μ\muμ, the probability that a computation takes path μ\muμ is independent of the input.
An approximate quantum state preparation method is introduced, called the Walsh series loader (WSL). The WSL approximates quantum states defined by real-value functions of single real variables with a depth independent of the number nnn of qubits. Two approaches are presented. The first one approximates the target quantum state by a Walsh series truncated at $O(1/\sqrt{\ensuremath{\epsilon}})$, where $\ensuremath{\epsilon}$ is the precision of the approximation in terms of infidelity. The circuit depth is also $O(1/\sqrt{\ensuremath{\epsilon}})$, the size is $O(n+1/\sqrt{\ensuremath{\epsilon}})$, and only one ancilla qubit is needed. The second method accurately represents quantum states with sparse Walsh series. The WSL loads sss-sparse Walsh series into nnn qubits with a depth doubly sparse in sss and kkk, the maximum number of bits with value 1 in the binary decomposition of the Walsh function indices. The associated quantum circuit approximates the sparse Walsh series up to an error $\ensuremath{\epsilon}$ with a depth O(sk)O(sk)O(sk), a size O(n+sk)O(n+sk)O(n+sk), and one ancilla qubit. In both cases, the protocol is a repeat-until-success procedure with a probability of success $P=\mathrm{\ensuremath{\Theta}}(\ensuremath{\epsilon})$, giving an averaged total time of $O(1/{\ensuremath{\epsilon}}^{3/2})$ for the WSL and $O(sk/\ensuremath{\epsilon})$ for the sparse WSL. Amplitude amplification can be used to reach a probability of success $P=\mathrm{\ensuremath{\Theta}}(1)$, modifying the quantum circuit size to $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{O}((n+1/\sqrt{\ensuremath{\epsilon}})/\sqrt{\ensuremath{\epsilon}})$ and $\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{O}((n+sk)/\sqrt{\ensuremath{\epsilon}})$ and the depth to $O([log{(n)}^{3}+1/\sqrt{\ensuremath{\epsilon}}]/\sqrt{\ensuremath{\epsilon}})$ and $O([log{(n)}^{3}+sk]/\sqrt{\ensuremath{\epsilon}})$, respectively. Amplitude amplification reduces by a factor $O(1/\sqrt{\ensuremath{\epsilon}})$ the total time dependence on $\ensuremath{\epsilon}$ but increases the size and depth of the associated quantum circuits, making them linearly dependent on nnn. These protocols give overall efficient algorithms with no exponential scaling in any parameter. They can be generalized to any complex-value, multivariate, almost-everywhere-differentiable function. The repeat-until-success Walsh series loader is so far the only method that prepares a quantum state with a circuit depth and an averaged total time independent of the number of qubits.
In quantum computing, every operation must be unitary to ensure reversibility and preserve probability amplitudes, which is a fundamental requirement for any quantum process. However, many real-world computations in areas such as machine learning, signal processing, and dynamical modeling inherently involve non-unitary matrices. Since these cannot be directly implemented on quantum hardware, a practical workaround is required. One promising solution is block-encoding, a framework that embeds a non-unitary matrix A in C^{n x n} into a larger unitary matrix U_A in C^{2n x 2n}, enabling the simulation of non-unitary actions within quantum circuits through ancilla preparation and post-selection. This thesis presents a novel and efficient method for block-encoding based on Gram-Schmidt orthogonalization. The algorithm constructs a unitary U_A with A embedded as the top-left block and remaining components filled to ensure global unitarity. Compared to traditional Singular Value Decomposition (SVD)-based approaches, the proposed method achieves at least 20% improvement in execution time, without compromising output fidelity or numerical stability. A corresponding quantum circuit is developed to extract A|psi> (that is, the transformed version of the input quantum state |psi> after applying the linear operator A) using post-selection on ancilla qubits, with the success probability determined by
I study a promise problem for an unknown unitary operator acting on an nnn-qubit system. The operator is promised to take one of two forms: either it implements a fixed permutation of computational basis states, or it implements the same permutation together with a conditional sign change determined by a designated input qubit. I show that these two cases can be distinguished with certainty using a single query to the unknown operator and a measurement of only one qubit. The procedure requires no ancilla qubits and uses only n+1n+1n+1 Hadamard gates in addition to the oracle call. The promise is intrinsically quantum, since the two cases differ only in their relative-phase structure and therefore have no direct classical counterpart in the usual black-box model.
One of the challenges in quantum computing is the synthesis of unitary operators into quantum circuits with polylogarithmic gate complexity. Exact synthesis of generic unitaries requires an exponential number of gates in general. We propose a novel approximate quantum circuit synthesis technique by relaxing the unitary constraints and interchanging them for ancilla qubits via block encodings. This approach combines smaller block encodings, which are easier to synthesize, into quantum circuits for larger operators. Due to the use of block encodings, our technique is not limited to unitary operators and can be applied for the synthesis of arbitrary operators. We show that operators which can be approximated by a canonical polyadic expression with a polylogarithmic number of terms can be synthesized with polylogarithmic gate complexity with respect to the matrix dimension.