Abstract The measurement-based quantum computing paradigm relies on entangling the qubits of a register into a graph state and on measuring subsets of such register in order to condition the unmeasured qubits. The computation therefore, instead of being carried on only by one-qubit and two-qubits logic gates, is largely based on entanglement and measurement processes. Compared to the gate model architecture, in MBQC the overhead in terms of computation resources to synthesize logical qubits is higher. While gate model relies on a register of logical qubits whose number is set at the beginning of the computation and remains constant during the computation, in the MBQC the input qubits outnumber the output ones, due to the destructive nature of measurement processes. Still, we analytically prove and experimentally confirm that MBQC can be efficiently emulated on a classical software, reaching equal loads with respect to the gate-based approach in terms of average runtime and storage of memory. The numerical results confirm that despite the potential computational overhead due to the high entanglement of the initial graph state, the MBQC paradigm carries similar computational complexity, both in terms of time and memory, with respect to the gate-based approach.
Abstract The measurement-based quantum computing paradigm relies on entangling the qubits of a register into a graph state and on measuring subsets of such register in order to condition the unmeasured qubits. The computation therefore, instead of being carried on only by one-qubit and two-qubits logic gates, is largely based on entanglement and measurement processes. Compared to the gate model architecture, in MBQC the overhead in terms of computation resources to synthesize logical qubits is higher. While gate model relies on a register of logical qubits whose number is set at the beginning of the computation and remains constant during the computation, in the MBQC the input qubits outnumber the output ones, due to the destructive nature of measurement processes. Still, we analytically prove and experimentally confirm that MBQC can be efficiently emulated on a classical software, reaching equal loads with respect to the gate-based approach in terms of average runtime and storage of memory. The numerical results confirm that despite the potential computational overhead due to the high entanglement of the initial graph state, the MBQC paradigm carries similar computational complexity, both in terms of time and memory, with respect to the gate-based approach.
The MaxCut is a combinatorial problem which belongs to the NP-hard class. The algorithm to solve it can be mapped into a quantum system, where the optimal configuration matches the ground state of a target Hamiltonian. It is possible to implement the adiabatic theorem into the circuit model via the QAOA algorithm, by arbitrarily choosing from both the gate-based and the measurement-based quantum computing (MBQC) settings. Recently, the MaxCut problem has been generalized to the Max K-Cut version also in the MBQC frame, where K stands for the number of cuts to implement. Here, the QAOA algorithm for a Max K-Cut is solved for a graph with arbitrary number of vertices V and number of cuts K scaling as a power of 2, along with the circuit depth p. The problem is solved on a emulator based on classical HPC resources. In order to test the performances of a Max K-cut implemented in the MBQC frame, we report how the average runtime and the approximation ratio scale by increasing V, K and p, comparing the results from two different topologies of graphs, 2-regular graphs with null weights on the diagonals versus the the fully-connected graphs. Eventually, the approximation ratio, evaluated as a parameter of success, is shown to yield optimal performances.
The measurement-based architecture is a paradigm of quantum computing, relying on the entanglement of a cluster of qubits and the measurements of a subset of it, conditioning the state of the unmeasured output qubits. While methods to map the gate model circuits into the measurement-based are already available via intermediate steps, we introduce a paradigm for quantum compiling directly converting any quantum circuit to a class of graph states, independently from its size. This method relies on the stabilizer formalism to describe the register of the input qubits. An equivalence class between graph states able to implement the same circuit is defined, giving rise to a gauge freedom when compiling in the MBQC frame. The graph state can be rebuilt from the circuit and the input by employing a set of graphical rules. A system of equations describes the overall process. Compared with measurement calculus, the ancillary qubits are reduced by 50% on the quantum Fourier transform and 75% on the Quantum Approximate Optimization Algorithm, still preserving similar scaling laws for the number of entangling gates.
Photonic quantum computers, programed within the framework of the measurement-based quantum computing (MBQC), and gate-based platforms are racing toward useful quantum advantage, and some algorithms have emerged as main candidates to reach this goal in the near term. The majority of these algorithms are only expressed in the gate-based model of computation, which is incompatible with photonic platforms. Methods to translate gate-based algorithms into the MBQC framework exist, but they are not always optimal in terms of resource cost. In our work, we propose an MBQC algorithm to run the quantum approximate optimization algorithm (QAOA). Furthermore, we apply the MBQC-QAOA algorithm to the Max K-Cut problem, for all values of K, expressing the cost Hamiltonian and its constraints in a form easily implementable in the MBQC model. We conclude by analyzing the resource cost of our algorithm, compared to the case of translating a gate-based QAOA algorithm into MBQC rules, and show up to a 30-fold improvement. With our work, we contribute to closing the gap between gate-based and MBQC near-term algorithms, a gap not reflecting the current status of the hardware development.
Abstract Measurement-based quantum computation (MBQC) using two-component Bose–Einstein condensates (BECs) is proposed in this paper. An arbitrary state for one logical qubit is obtained by three-body measurement. Furthermore, a method is proposed for implementing controlled-Z gates for logical qubits in a graph state using BEC qubits and controlled-Z gates for BEC-type encoding. Results showed that the state after a measurement depends on the number of particles. These results pave the way for a novel quantum computing process based on particle control.
Measurement-Based Quantum Computing (MBQC) is inherently well-suited for Distributed Quantum Computing (DQC): once a resource state is prepared and distributed across a network of quantum nodes, computation proceeds through local measurements coordinated by classical communication. However, since non-local gates acting on different Quantum Processing Units (QPUs) are a bottleneck, it is crucial to optimize the qubit assignment to minimize inter-node entanglement of the shared resource. For graph state resources shared across two QPUs, this task reduces to finding bipartitions with minimal cut rank. We introduce a simulated annealing-based algorithm that efficiently updates the cut rank when two vertices swap sides across a bipartition, such that computing the new cut rank from scratch, which would be much more expensive, is not necessary. We show that the approach is highly effective for determining qubit assignments in distributed MBQC by testing it on grid graphs and the measurement-based Quantum Approximate Optimization Algorithm (QAOA).
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.