In standard quantum computation, the initial state is pure and the answer is determined by making a measurement of some of the bits in the computational basis. What can be accomplished if the initial state is a highly mixed state and the answer is determined by measuring the expectation of ${\ensuremath{\sigma}}_{z}$ on the first bit with bounded sensitivity? This is the situation in high temperature ensemble quantum computation. We show that in this model it is possible to perform interesting physics simulations that have no known efficient classical algorithms, even though the model is less powerful than standard quantum computation in the presence of oracles.
In standard quantum computation, the initial state is pure and the answer is determined by making a measurement of some of the bits in the computational basis. What can be accomplished if the initial state is a highly mixed state and the answer is determined by measuring the expectation of \ensuremathσz on the first bit with bounded sensitivity? This is the situation in high temperature ensemble quantum computation. We show that in this model it is possible to perform interesting physics simulations that have no known efficient classical algorithms, even though the model is less powerful than standard quantum computation in the presence of oracles.
The ``power of one qubit'' refers to a computational model that has access to only one pure bit of quantum information, along with n qubits in the totally mixed state. This model, though not as powerful as a pure-state quantum computer, is capable of performing some computational tasks exponentially faster than any known classical algorithm. One such task is to estimate with fixed accuracy the normalized trace of a unitary operator that can be implemented efficiently in a quantum circuit. We show that circuits of this type generally lead to entangled states, and we investigate the amount of entanglement possible in such circuits, as measured by the multiplicative negativity. We show that the multiplicative negativity is bounded by a constant, independent of n, for all bipartite divisions of the n+1 qubits, and so becomes, when n is large, a vanishingly small fraction of the maximum possible multiplicative negativity for roughly equal divisions. This suggests that the global nature of entanglement is a more important resource for quantum computation than the magnitude of the entanglement.
Quantum algorithms for problems such as integer factorization and database search have spurred great interest in quantum computation. However, the construction of scalable quantum computers has proved to be extremely difficult. Among the challenging requirements that must be fulfilled in the “standard model” of quantum computation, one must be able to initialize qubits into known pure states. In addition, quantum computers must be protected from noise due to unwanted interactions with their environment.
This work is concerned with two theoretical issues motivated by these difficulties. First, I analyze the efficiency of an algorithm for an alternate model for quantum computation that is motivated by bulk nuclear magnetic resonance. In such ensemble systems, the initial state is not a fiducial pure state, but is instead a maximally-mixed density operator. The algorithm that I consider allows one to estimate the free energy of arbitrary spin-1/2 lattice models. To determine the efficiency of this algorithm, I calculate the computation time required to estimate the free energy to bounded error as a function of the lattice size. In the absence of stochastic fluctuations in the measurement output, it is found that the algorithm is efficient. However, evidence is presented that suggests that the algorithm becomes exponentially sensitive to fluctuations as the lattice size increases.
While techniques such as quantum error-correcting codes and decoherence-free subsystem encodings have been devised to mitigate errors due to unwanted environmental couplings, these methods require many additional qubits or complicated encodings. Here, I investigate a simple approach to reduce errors in the quantum search algorithm due to a collective decoherence model. This method takes advantage of the freedom inherent in compiling the search algorithm into fundamental gates. Transition rate calculations and more rigorous quantum master equation simulations are carried out for small-qubit instances to contrast the performance of the original and modified algorithm. It is shown that the expected computational effort can be reduced by 22% for selected five-bit instances of the quantum search algorithm. While this approach does not constitute a general strategy for quantum error-correction, it illustrates the importance of judicious gate design in mitigating decoherence.
We present a model of quantum metrology inspired by the computational model known as deterministic quantum computation with one quantum bit (DQC1). Using only one pure qubit together with l fully mixed qubits we obtain measurement precision (defined as root-mean-square error for the parameter being estimated) at the standard quantum limit, which is typically obtained using the same number of uncorrelated qubits in fully pure states. In principle, the standard quantum limit can be exceeded using an additional qubit which adds only a small amount of purity. We show that the discord in the final state vanishes only in the limit of attaining infinite precision for the parameter being estimated.
Deterministic quantum computation with one quantum bit (DQC1) [E. Knill and R. Laflamme, Phys. Rev. Lett. 81, 5672 (1998)] is a model of quantum computing where the input is restricted to containing a single qubit in a pure state and has all other qubits in a completely mixed state. Only the single pure qubit is measured at the end of the computation. While it is known that DQC1 can efficiently solve several problems for which no known classical efficient algorithms exist, the question of whether DQC1 is really more powerful than classical computation remains open. In this Letter, we introduce a slightly modified version of DQC1, which we call DQC1(k), where k output qubits are measured, and show that DQC1(k) cannot be classically efficiently simulated for any k≥3 unless the polynomial hierarchy collapses at the third level.
This thesis considers various paradigms of quantum computation in an attempt to understand the nature of the underlying physics. A standard approach is to consider unitary computation on pure input states, such that the outcome of the computation is determined by single computational basis measurement on the output state. It has been shown that there exists equivalent models of computation, such as measurement based quantum computing (MBQC), which provide insight into the role of entanglement and measurement. Furthermore, constraining or relaxing available resources can directly impacts the power of the computation, allowing one to gauge their role in the process. Here, we first extend known constructions such as Matrix Product States, MBQC and the oneclean qubit model to a mixed state formalism, in an attempt to develop computational models where noise acting on the physical resources, as might be experienced in laboratory settings, may be mapped to logical noise on the computation. Next, we introduce Measurement-Based Classical Computing, an essentially classical model of computation, wherein the complexity hard wired into probability distributions generated via quantum means yields surprising non classical results. Finally, we consider postselection the ability to discard displeasing measurement outcomes and argue that it may be used in a tame way, which does not provide a dramatic increase in computational power. From here, we develop a new Hamiltonian, based on a circuit to Hamiltonian construction, presenting evidence of QMA-hardness.
Deterministic quantum computation with one quantum bit (DQC1), or the one clean qubit model, [E. Knill and R. Laflamme, Phys. Rev. Lett. {\bf81}, 5672 (1998)] is a model of quantum computing where the input is the tensor product of a single pure qubit and many completely-mixed states, and only the single qubit is measured at the end of the computation. In spite of its naive appearance, the DQC1 model can efficiently solve some problems for which no classical efficient algorithms are known, and therefore it has been conjectured that the DQC1 model is more powerful than classical computing (under the assumption of $\mbox{BPP}\subsetneq\mbox{BQP}$). Here we show that the output probability distribution of the DQC1 model cannot be classically efficiently approximated (exactly within a polynomial bit length or in the fully polynomial randomized approximation scheme (FPRAS) with at most a constant error) unless $\mbox{BQP}\subseteq\mbox{BPP}$.