The official community for QuantumSpark researchers. Discuss physics, share code, and collaborate on the future of computation.
Surface codes are currently the most promising candidate for fault-tolerant quantum computing due to their high error threshold. Recently, I've been experimenting with the $d=3$ surface code. The logical qubit is encoded in a grid of physical qubits where the stabilizers are: $$Z_p = \prod_{i \in p} Z_i, \quad X_p = \prod_{i \in p} X_i$$ We're seeing a significant improvement in logical error rates as we scale, but the decoders (matching algorithms) are still a bottleneck. What's everyone's favorite MWPM implementation?
Standard fault-tolerance proofs often assume Markovian noise (independent per time step). However, in many superconducting platforms, 1/f noise and cosmic ray events introduce significant temporal and spatial correlations. Numerical Analysis: We've been simulating the surface code under a correlated noise model where error rates $p(t)$ are coupled across multiple cycles. We observe a significant shift in the threshold from ~1% down to ~0.4% depending on the correlation length $\xi$. Theoretical Questions: - Can **Decoupling Sequences** effectively 'Markovianize' the noise before the syndrome extraction? - How do **3D Gauge Color Codes** handle these correlations compared to the standard Toric code? Check out this code snippet for a basic depolarizing noise sim under correlation: ```python def correlated_noise(qubits, p, correlation_factor): # Simplified simulation of spatially correlated errors for i in range(len(qubits)): if random.random()
The relationship between black hole thermodynamics and quantum information theory has never been more profound. The ER=EPR conjecture suggests that entanglement (EPR pairs) is fundamentally linked to geometric structures (Einstein-Rosen bridges). Computational Implications: Scrambling time $t_* \sim \beta \log(S)$ is the theoretical limit at which information is spread across all degrees of freedom. In holographic duals, this corresponds to the fast scrambling of the event horizon. Discussion Items: 1. **OTOCs (Out-of-Time-Order Correlators)**: Can we measure these in current trapped-ion systems to verify holographic scrambling scales? 2. **Circuit Complexity**: Is the 'Complexity = Action' or 'Complexity = Volume' conjecture more consistent with modular Hamiltonian evolution? 3. **Tensor Networks**: Using MERA (Multi-scale Entanglement Renormalization Ansatz) as a discrete model for the hyperbolic geometry of AdS bulk. Let's discuss the role of **Quantum Chaos** in the dual CFT.
A massive milestone in the quest for Fault Tolerance. They used an active syndrome extraction protocol. This could be the end of the Noisy Intermediate-Scale (NISQ) era.
Just a quick update—our community has grown 3x in the last month. We're seeing amazing research projects coming out of the High School Fellowship. Keep it up everyone! Next stop: Universal Fault Tolerance (and maybe 20k users).
[Image of a skeleton at a computer] - When the T1 time is shorter than the transpilation time. #QuantumLife
Post your findings here! AWS, IBM, Google, and IonQ have already opened their applications. Xanadu has a new residency program for grad students too.
It's that time of year again! Post all your findings for summer positions here. **Current Openings:** 1. Google Quantum AI - Summer Scholar (Santa Barbara) 2. Xanadu - Software Intern (Toronto/Remote) 3. PsiQuantum - Photonic Systems Intern (Palo Alto) Add any others below!
The US government is doubling down on reaching 1M physical qubits. They are looking for proposals that address interconnect efficiency and cooling.
The transition from `execute` to `Sampler` and `Estimator` is quite a shift. Here is a quick cheat sheet I made for my research team.
Financial sovereignty relies on RSA/ECDSA. Should the quantum community prioritize developing remediation tools for the public before selling to big banks?
Technically, until the bartender looks at the door, the cat is both inside the bar and outside on the sidewalk. But the real question is: Does the bartender count as an observer or is he just part of the entangled system? Spooky action at a distance? More like spooky service at the bar.
I've been building a simulator that exploits Julia's multiple dispatch for high performance. Check out the GitHub repo and let me know if you want to contribute!
This Google paper from 2024 is the gold standard for state preparation. I've summarized the key circuit diagrams and syndrome extraction steps here.
IBM and Google are betting big on superconducting qubits, while Quantinuum and IonQ use trapped ions. Which approach do you think will reach 1,000 logical qubits first?
Next week 1.0 drops. The `execute` function is basically gone, replaced entirely by `Sampler` and `Estimator` primitives. I'm working on a compatibility layer for our Playground. If your local scripts break, check the new Transpiler pass manager logic.
I made a tool that shows how the multi-party entanglement entropy changes as you apply CNOT gates. Great for students learning about Bell states.
I've interviewed at 3 startups this year. Most just want to see a GitHub repo with clean, documented Qiskit code and a solid grasp of error mitigation.
Photonics don't require massive dilution fridges and can travel at light speed between processors. Xanadu is making serious progress. What's the catch?
Surface codes rely on stabilizer measurements to identify errors. I've been looking at d=5 grids. The decoding overhead is still the biggest challenge.
I've been building a custom state-vector simulator using WebGL that can handle up to 20 qubits in real-time, and I'm optimizing a sparse-matrix version for the QuantumSpark playground. Check out this demo of a GHZ state preparation!
I've been benchmarking both on Max-Cut problems. QAOA seems more robust to noise, but VQE converges faster for chemistry problems. Let's compare results.
We mapped a 50-asset portfolio to an Ising model and ran it on D-Wave. The results were comparable to Gurobi but showed interesting tail-risk insights.
Most researchers believe BQP is larger than P, but smaller than NP. What are the current strongest proofs for this separation? Let's discuss the Oracular model.
The recent paper from PennyLane shows a clear quantum advantage in specific high-dimensional datasets. I'm trying to replicate this using the Iris dataset.
This paper proves that access to a quantum memory can exponentially speed up certain learning tasks. Let's discuss the 'quantum principal component analysis' part.
QAE offers a square-root speedup over classical Monte Carlo. I've implemented a simple European Option pricer in Qiskit Finance. Check it out!
I want to work on algorithm development. Should I focus on the underlying physics or stay on the CS side and learn the math? Jobs seem to favor CS/Math lately.
We successfully ran a simple classification task on real hardware. The accuracy was lower than simulation, but the noise models are getting better.
Hardware access is expensive. How do we ensure that the next generation of researchers isn't limited to a few wealthy institutions?
Tanner graphs and belief propagation are standard for LDPC. Can we map these effectively to a 2D grid without needing an insane number of SWAPs?
I've implemented a scaled-down version of Shor's for factoring 15. The modular exponentiation step is definitely the trickiest to optimize on real hardware.
If you only use Clifford gates, your circuit is perfectly simulatable on a classical computer. Why then is the T-gate so expensive and so important?
I'm a CS major. I love quantum, but I'm worried about the 'Quantum Winter'. Should I focus on the SDK/Compiler side (Qiskit/Cirq) or go deep into the physics to do algorithm research? Which skills are more transferable?
Just found out about the new Moore Foundation grant for quantum research. Deadline is November 1st. Good luck everyone!
Was it the physics, the math, or the potential for breaking encryption? For me, it was seeing a Bloch sphere for the first time and realizing how different computing could be.
I spent 3 hours wondering why my gate counts were so high. Turns out I was running a surface code without mapping it to the backend's heavy-hex coupling map.
BB84 protocol is great in the lab, but environmental noise in commercial fibre makes it difficult to maintain a high secret key rate. Thoughts on Twin-Field QKD?
I love PennyLane's autograd capabilities, but Cirq feels closer to the hardware metal. Which one are you using for your PhD thesis?
Any state $|psi angle = cos( heta/2)|0 angle + e^{iphi}sin( heta/2)|1 angle$ can be mapped to a point on a sphere. But why do we use $ heta/2$? Hint: It's because the physical space is $CP^1$, not $S^2$. Let's discuss the Hopf fibration!
I found a great template that includes TikZ libraries for drawing quantum circuits properly. Sharing the link here for all candidates.
On-demand single photons are the holy grail. Quantum dots have better purity, but SPDC is easier to integrate. Let's compare recent experimental data.
As we move towards the 1000-qubit regime, the interconnect architecture becomes the primary constraint. Superconducting (Transmon) Platforms: - **Pros**: High gate speeds (ns), established lithography. - **Cons**: 2D connectivity limits (nearest-neighbor), massive cryogenic overhead for coaxial wiring. Trapped-Ion (QCCD) Platforms: - **Pros**: All-to-all connectivity within a zone, long coherence times ($T_2$). - **Cons**: Slow gate speeds ($\mu s$), complexity of laser/optical switching at scale. Is the **Photonic Interconnect** (transducing microwave photons to optical) the only viable path for modular superconducting scaling? Or should we focus on 3D integration with TSVs (Through-Silicon Vias)?
The quest for Majorana zero modes (MZMs) has entered a critical phase. While initial results in InAs/Al systems were promising, the field has transitioned towards more rigorous protocols for identifying topological transitions. Key Discussion Points: 1. **Zero-Bias Conductance Peaks (ZBCP)**: Are they sufficient evidence? We need to discuss the impact of quasi-particle poisoning and Andreev bound states that mimic MZM signatures. 2. **Braiding Statistics**: The next frontier is demonstrating non-Abelian statistics. Current proposals for T-junctions and hexon/tetron architectures are mathematically sound, but engineering the charging energy $E_C$ remains a hurdle. 3. **Materials Science**: Transitioning from III-V nanowires to topological insulators (like $Bi_2Se_3$) or 2D platforms. What are your thoughts on the recent 'string' proposals for topological protection?
The recent Google paper is a milestone. They achieved logical error rates below physical error rates for the first time with a $d=5$ code. The key was the 'distance-preserving' state preparation. Let's discuss the stabilizer measurement circuits used.
How are you dealing with vanishing gradients in your QNNs? I've started using 'Local Cost Functions' and it seems to help with the barren plateau problem.
I'm a double-espresso person when I'm debugging Clifford gates. What about you? The lounge is open for all non-physics talk!
With the advancement of Shor's algorithm, traditional RSA and ECC are at risk. NIST has been evaluating PQC candidates like CRYSTALS-Dilithium and Falcon. These rely on lattice-based problems like Shortest Vector Problem (SVP): $$|| \sum a_i b_i || \le L$$ I've written a small script to verify the signature size trade-offs. Lattices are beautiful but the key sizes are massive compared to ECC. Thoughts?
Use double dollar signs for display equations: `$$\psi = \alpha|0\rangle + \beta|1\rangle$$`. Use single dollar signs for inline: `$\pi/2$`. This makes your research much easier to read! We support most standard MathJax symbols.
Keeping qubits at mK temperatures is expensive and scales poorly. New pulse-tube cooler designs are hitting the market. Has anyone seen the latest Bluefors specs?
I get the math, but I don't intuitively 'feel' how the oracle bit flips the phase of the control qubit. Can someone explain it like I'm 5?
The decay curve gives the average gate error. But how do we distinguish between coherent and incoherent noise from a standard RB plot?
We just finished a benchmarking run comparing the gate fidelity and CLOPS (Circuit Layer Operations Per Second) on the latest architectures. While IBM has a lead in raw qubit count, Rigetti's tiling architecture is showing interesting scaling for certain Variational Algorithms. Has anyone else pulled the latest Qiskit-Experiments data for these?
Just finished 'Recursion' by Blake Crouch. Any other books that handle quantum superposition well? Looking for my next summer read.
Unlike superconducting qubits, photons don't need a dilution refrigerator. They can travel through room-temperature fiber optics. The main challenge? Interaction. How do we make two photons talk to each other reliably?
Simulating a 54-qubit Sycamore circuit at depth 20 is the current benchmark for classical supremacy verification. Current Landscape: 1. **State Vector (Schrödinger)**: Requires $sim 2^{54}$ complex amplitudes. Even with NVIDIA cuQuantum and massive multi-GPU clusters, memory bandwidth is the killer. 2. **Tensor Networks (Feynman)**: Slicing and contracting the circuit graph. Highly efficient for low-depth circuits, but complexity scales exponentially with entanglement (treewidth). Benchmarks: We've been using **MPS (Matrix Product States)** to simulate large-scale VQE ansatze. For bond-dimension $\chi = 1000$, we can handle 100+ qubits for shallow circuits. When does the **Tree Tensor Network (TTN)** approach become superior for chemistry simulations in terms of multi-node scaling?
Beginners often ask why we need complex numbers and Hilbert spaces. In quantum mechanics, a state is a unit vector in a complex Hilbert space $\mathcal{H}$. The transition probability between states $|psi angle$ and $|phi angle$ is given by: $$P = |langle phi | psi angle|^2$$ Without the inner product structure of Hilbert spaces, we wouldn't have a consistent way to define measurement probabilities.
Is there a way to do mid-circuit measurements and then feed-forward in the current IBM primitives? I keep getting state preparation errors.
Running on `ibm_brussels`. The counts for |000> and |111> are high, but I see a lot of noise in between. Is this T1 decoherence or just readout error?
A deep dive into Continuous Variable (CV) quantum computing. We're breaking down how squeezed light can be used for universal fault-tolerance.
I'm trying to run a Variational Quantum Eigensolver (VQE) for $H_2$ on a noisy backend, but the energy minimum keeps drifting. I suspect it's the Readout Error. Here's my ansatz setup: ```python from qiskit.circuit.library import EfficientSU2 ansatz = EfficientSU2(2, reps=1) # ... optimization loop ... ``` Has anyone had success with Error Mitigation techniques like PEC or ZNE for this?
Working on a new modular approach to surface code simulation. Looking for collaborators to test on specialized hardware.
I recommend we adopt the Qiskit-v2 reviewing standards for all student submissions starting next month. What are your thoughts on integrating automated circuit depth checks?