Experiments with Generalized Quantum Measurements and Entangled Photon Pairs
Bibliographic record
Abstract
This thesis describes a linear-optical device for performing generalized quantum measurements \non quantum bits (qubits) encoded in photon polarization, the implementation \nof said device, and its use in two diff erent but related experiments. The device works by \ncoupling the polarization degree of freedom of a single photon to a `mode' or `path' degree \nof freedom, and performing a projective measurement in this enlarged state space in order \nto implement a tunable four-outcome positive operator-valued measure (POVM) on the \ninitial quantum bit. In both experiments, this POVM is performed on one photon from a \ntwo-photon entangled state created through spontaneous parametric down-conversion. \nIn the fi rst experiment, this entangled state is viewed as a two-qubit photonic cluster \nstate, and the POVM as a means of increasing the computational power of a given resource \nstate in the cluster-state model of quantum computing. This model traditionally \nachieves deterministic outputs to quantum computations via successive projective measurements, \nalong with classical feedforward to choose measurement bases, on qubits in a highly entangled \nresource called a cluster state; we show that `virtual qubits' can be appended to a \ngiven cluster by replacing some projective measurements with POVMs. Our experimental \ndemonstration fully realizes an arbitrary three-qubit cluster computation by implementing \nthe POVM, as well as fast active feed-forward, on our two-qubit photonic cluster state. \nOver 206 diff erent computations, the average output delity is 0.9832 +/- 0.0002; furthermore \nthe error contribution from our POVM device and feedforward is only of order 10^-3, less \nthan some recent thresholds for fault-tolerant cluster computing. \nIn the second experiment, the POVM device is used to implement a deterministic \nprotocol for remote state preparation (RSP) of arbitrary photon polarization qubits. RSP \nis the act of preparing a quantum state at a remote location without actually transmitting \nthe state itself. We are able to remotely prepare 178 diff erent pure and mixed qubit \nstates with an average delity of 0.995. Furthermore, we study the the fidelity achievable \nby RSP protocols permitting only classical communication, without shared entanglement, \nand compare the resulting benchmarks for average fidelity against our experimental results. \nOur experimentally-achieved average fi delities surpass the classical thresholds whenever \nclassical communication alone does not trivially allow for perfect RSP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".