Crucially, the conditions for passing the protocol-the presence of stable ZBPs at both ends of the device over a gapped region with gapless boundary, as established via the non-local conductance-were established before any devices had been measured. Our simulations have demonstrated that the TGP is a very stringent criterion, rendering it a reliable method for detecting the topological phase in a device. We have simulated our devices using models that incorporate the details of the materials stack, geometry, and imperfections. Figure 1: The local density of states of a topological superconducting nanowire as a function of energy and position. Such a transition must be present at the boundary between the trivial superconducting phase and the topological phase because these are two distinct phases of matter, as different as water and ice. Thus, the TGP focuses on ZBPs that are highly stable and, crucially, uses the non-local conductance to detect a bulk phase transition. However, local Andreev bound states and disorder can also cause zero-bias peaks. In electrical measurements, the Majorana zero modes (see Figure 1) cause zero-bias peaks (ZBPs) in the local conductance.

There is a real fermionic operator localized at each end of the wire, analogous to the real fermionic wave equation constructed by Ettore Majorana in 1937.Ĭonsequently, there are two quantum states of opposite fermion parity that can only be measured through a phase-coherent probe coupled to both ends. Topological superconducting wires have Majorana zero modes at their ends. This difficulty was addressed by the “ topological gap protocol” (TGP), which our team set forth a year ago as a criterion for identifying the topological phase with quantum transport measurements. Doing so is not trivial because topological phases are characterized by the long-ranged entanglement of their ground states, which is not readily accessible to conventional experimental probes. This path is only open if one can reliably produce a topological phase of matter and experimentally verify that the sub-components of a qubit are in a topological phase (and ready for quantum information processing). The fidelity, speed, and size of a topological qubit is controlled by a characteristic energy called the topological gap. Topological quantum computation is a route to hardware-level fault tolerance, potentially enabling a quantum computing system with high fidelity qubits, fast gate operations, and a single module architecture. Microsoft Quantum team reports observation of a 30 μeV topological gap in indium arsenide-aluminum heterostructures Chetan Nayak, Distinguished Engineer of Quantum at Microsoft to discuss Microsoft’s unique approach to building a fully scalable quantum machine. Sankar Das Sarma, a Distinguished University Professor of Physics at University of Maryland joins Dr. L earn more about our journey and visit Azure Quantum to explore the Azure Quantum hardware profolio and get started with quantum computing today.ĭr. Sankar Das Sarma recently sat down to discuss these results and why they matter in the video below. While engineering challenges remain, this discovery proves out a fundamental building block for our approach to a scaled quantum computer and puts Microsoft on the path to deliver a quantum machine in Azure that will help solve some of the world’s toughest problems.ĭr. The explanation of our work and methods below shows that the underlying physics behind a topological qubit are sound-the observation of a 30 μeV topological gap is a first in this work, and one that lays groundwork for the potential future of topological quantum computing. We have discovered that we can produce the topological superconducting phase and its concomitant Majorana zero modes, clearing a significant hurdle toward building a scaled quantum machine.

Microsoft is taking a more challenging, but ultimately more promising approach to scaled quantum computing with topological qubits that are theorized to be inherently more stable than qubits produced with existing methods without sacrificing size or speed. Today’s quantum computers are based on a variety of qubit types, but none so far have been able to scale to enough qubits to fully realize the promise of quantum. However, these researchers often find themselves limited by the inadequate scale of these systems and are eager to do more. Today’s quantum computers are enabling researchers to do interesting work. Yet as an industry, we are still in the early days of discovering what’s possible. Quantum computing promises to help us solve some of humanity’s greatest challenges.

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